AI, startup hacks, and engineering miracles from your friends at Faraday

For Customer Success, email drafts should be warmup laps

Thomas Bryenton on

Technical topics often tempt Customer Success Managers/CSMs to type up all the info they can think of and launch it at the client's inbox like a cannonball.

This is usually a mistake -- but the mistake isn't writing the draft, it's pressing send.

Here's a better path.

  1. Write out an email that has all the key points but would still be reasonable to send
  2. Instead of sending, send a message like this: "Hi client, would you have time to review X on the phone later today, maybe 3pm ET?"
  3. Use your email as talking points

Assuming the CSM feels comfortable talking with clients, whatever the complicated or bad news is, the call will go better than the email would have.

What do you do with the email you wrote out so carefully, besides use it as talking points? Send it:

  • either as a followup to the client confirming what you already discussed
  • or to yourself, and give it a label like "Emails I was thoughtful enough not to send" so that you can look back and see how concise you've been

Operationalize AI quickly and cost-efficiently with turnkey solutions

Perry McDermott on

Turnkey AI solutions

If you've got your finger on the pulse of your business, you know you need to adopt AI sooner than later.

While commercial AI adoption is on the rise, there's still time to catch up and get ahead of the curve with "off-the-shelf", or turnkey AI solutions.

Where do you stand in the AI landscape?

Consider the following questions:

  • Are you brand new to AI? If so, where will AI have an immediate impact on your top and bottom lines?
  • Are you experimenting with an enterprise-wide AI solution? If so, are you seeing the results you were expecting?
  • Are you already developing AI in-house? If so, what can you do to optimize your existing resources?

Regardless of your position in the AI landscape, you can quickly get started with AI or optimize your existing resources by implementing proven, turnkey AI solutions that address the low-hanging fruit.

What are the low-hanging fruit?

It's all about automating tasks that have an immediate impact on revenues and costs.

Rather than using AI to automate existing jobs, use it to improve efficiency and productivity within those jobs.

After working with hundreds of B2C companies in a wide range of industries, we found that the customer lifecycle is an ideal framework for discovering highly predictive data, applying machine learning, and developing predictive models for a variety of use cases.

Let's take a quick look at a couple popular use cases to get you thinking about how they might apply to your business.

Increasing conversion rates and reducing acquisition costs

The most effective way to optimize your spend throughout the customer acquisition process is by targeting individuals with the highest propensity to convert.

So how do we identify individuals who are likely to convert?

  • The first step is to enrich your lead and customer data with additional demographic, psychographic, and property-based attributes, giving you a vibrant picture of who your leads and customers are, not just what they purchased or where they came from.
  • The next step is to use that enriched data to train the AI that will ultimately make predictions on future data inputs.
  • Once trained, the AI can make data-driven predictions on who your most likely buyers are, where to reach them, and when to reach out.

We've seen projected sales cost reductions of over $300,000 when applying this approach.

Proactively preventing customer churn

Certain companies have a notorious problem with customer churn, especially those with subscription-based business models. Using a similar approach as the one mentioned above, companies can use AI to identify churn-prone customers before they even begin showing signs of churn.

This can help minimize marketing dollars spent on these churn-prone individuals, and focus more on customers with a higher lifetime value (LTV).

A deeper dive into use cases for turnkey AI

Hopefully, you're thinking about how you could leverage a turnkey AI solution to optimize outreach initiatives across your customer lifecycle.

For a deeper dive into the opportunities available with turnkey AI, check out our free whitepaper, AI or die: Understanding the inevitability of AI in business.

AI or die: Understanding the inevitability of AI in business

For more information on to use AI to optimize your customer lifecycle outreach, download our other whitepaper, AI for the customer lifecycle: Making the most of your data.

Deleting /var/lib/docker with devicemapper

Seamus Abshere on

If you're running docker with devicemapper as the storage driver, there are a few steps to recover after deleting /var/lib/docker:

$ sudo service docker stop
$ sudo rm -rf /var/lib/docker
$ sudo lvremove docker
$ docker-storage-setup
$ sudo service docker start

This will help you recover from errors like

Error starting daemon: error initializing graphdriver: devicemapper: Non existing device docker-docker–pool  


Error starting daemon: error initializing graphdriver: Unable to take ownership of thin-pool (dockerVG-docker--pool) that already has used data blocks  

Thanks to hints from redhat bugzilla and docker forums.