 losing the conversation history or the brand experience.
What are some examples of automation use cases across different industries?
For eCommerce brands, we recommend deploying automations for the following scenarios:
- Segmentation. Tailoring the experience for specific customers makes sense. If you have a segment of customers with a high Lifetime Value (LTV), you are likely going to treat them differently. This can be as simple as assigning them to a specific queue with agents who are designated for handling high value LTV customers.
- Orders. Order-specific issues around shipping may have shorter SLAs due to time sensitivity. Automations can be created to route these issue types to a high priority queue.
- Seasonality or Promotions. Perhaps you are running a promotion or your Black Friday and Cyber Monday sale. Automations can be used to auto-respond to users with promotion and seasonal-specific messaging around response times. Alternatively, these issues could be routed and assigned to a queue responsible for these specific issue types.
For gaming brands, we recommend deploying automations for the following scenarios:
- Tiered Players. You may want to handle a specific set of customers (VIPs) or those who spend a specific amount separately — so you can use automations to provide different messaging and different teams to handle free vs. paid gamers.
- Multiple Games or Apps. If you have multiple games, automations can help route to game-specific queues to keep issues organized.
- Multiple Languages. If you support multiple languages, automations can help route language-specific issues to language-specific queues, or provide automated responses in that language.
What specifically is the Helpshift platform able to automate?
I touched on what automations can do and how to combine these functions with other Helpshift features. Here’s a list.
- Automatically assign issues to agents and queues
- Automatically tag issues
- Trigger custom bots based on issue types or keywords
- Send auto-replies based on issue type or time of day (business hours)
- Update issue status automatically
- Reply automatically to issues and resolve those that don’t need agent attention, such as feedback
- Combine with AI-powered issue classification to run specific automations based on issue labels
- Combine with custom bots to automate routine steps in a conversation
- Run in-app, end-to-end workflows within asynchronous messaging conversations
The most encouraging part: implementing and setting up automations is simple to do.
Automations help brands respond to customers faster, maintains SLAs, prioritize issues based on business-rules or segmentation, and enable automatic follow-up with customers. Leveraging automations alongside Helpshift’s other features such as AI-powered issue classification, custom bots, and asynchronous messaging, allows your customer service team to elevate your customer support experience and massively improve on existing levels of efficiency.
See our automations in action and request a demo today!Another old one from my Utube channel. I came across it having a bit of a "spring clean" and realised that I had'nt uploaded it here.
I would like to see many more of our members uploading stuff on Tradconnect and adding to our archive. It has great potential as a means of sharing tunes.
Lovely tune Mike, and well played. I don't think you'd want to hear my fiddle playing online, at least not for the next few years anyway!!!
Fine whistle play!
Good one Mike :-) Happy to see you're still out there :-) Hope to see you online one of these days, we have some catching up to do I think....Is it bad for professors/supervisors to have their PhD students drop out?
Does it hurt the professor's career in any way if their students drop out? Assume a PhD student gets the professor's funding and publishes an adequate amount of papers. They then decide to quit (maybe with a master's). What benefit is it to the professor for the student to continue working?
Professors seem to be concerned about taking students who might not survive the entire program, but I don't see what the problem is.
For reference, I am particularly interested in the USA within CS.
See also: If I quit my PhD, would it make my advisor look bad?
Why do you need to know this, i.e. what's your goal here? I'm asking because this consideration should have next to zero bearing on your decision to quit.
Just a warning for future visitors: The answer really depends on the country and the type of PhD position.
From the title I expected this question was asking if it is advisable to join the group of a professor who has had many students quit. I would have emphatically said no to this question. For that reason, it is not good for a professor to have many students quit: it would look like they are somehow driving students to quit.
I am a bit hesitant to answer this because I guess the answer might put people off making a decision that is the correct decision for them, but honesty is always best, so the answer is yes, it can hurt a supervisor for their students to leave.
Where I am, the % of students who submit a thesis within time limit is a key metric on which an academic is judged. One reason for this is that government funding for PhD programs requires that submissions within time limit are above a certain threshold. This is also the reason that many universities will not initially register a student for a PhD, but will require the students to undergo a confirmation review after a year - if the student is dismissed at this point, it doesn't count against the department, but after this it does.
An academic will probably not suffer too much from a single student not completing in time (although yes, they will lose the right to say "my graduation rate is perfect" on a promotion case or job application). More than one and questions will definitely be asked, and that PI might start to find it more and more difficult to win the right to recruit students.
However, none of this should affect a student's decision as to whether continuing a PhD is right for them or not, and the damage done to a student staying in a programme when it is not right for them massively out-weighs the damage done to the supervisor if they leave.
Good point. I missed that the OP has stated they were in the US. I am not, this is the UK, but I believe it applies to other European countries and Australia as well. I'll edit to make clear.
UK academia is a bit too metric-driven, which ignores the reason why the student drops out. A PhD is a good way of finding out whether a career in research is for you. If you find that out early on that it isn't and decide to withdraw, that is a success rather than a failure.
@EarlGrey I'm genuinely interested in how advising students to make the decision that is correct for them relies on an "assumption that PhDs should think of their supervisor as the might entities that allowed them the chance to enter academia, putting their career at risk".
@EarlGrey You are right on several points: some of the selection metrics as used currently, without context, are fairly nonsensical. There is a lot of academic that hope context would be taken in to account in their applications. However, the unlucky supervisor who's first student was left unable to complete due to an injury might not get the chance to explain their context as they might be skipped over in a shortlisting process in favour of a more lucky colleague who's first student completed in time. And just because there was no intent to do damage, does not mean the damage does not exist.
" the students make this process so interior to them that then we have answer like yours or from the poster saying that the student damages the career of the supervisor" I specially said that students should look to what is best for them. I was directly trying to convince any students reading this not to blame themsevles for the damage to supervisors.
That would depend on the reasons that students drop out. Some will do so for health and family reasons. Some leave because they have decided to follow a different path. None of that reflects on the advisor.
But if students (plural) drop out alleging abuse by the advisor, personal or professional, then it certainly should reflect badly, though some get away with bad behavior for a long time.
Then why are admissions so worried about whether or not a student will finish the program?
The administration is also concerned with effective use of resources. When students drop out the program isn't efficient/effective. Everyone wants students who are highly likely to be successful, but there are no guarantees.
@JobHunter69 When there are more candidates than positions, why take people who won't finish when you could take someone who will? Taking on a student is putting your resources toward training someone.
However, the effective use of resources can very well also mean dismissing an ineffective student instead of trying to lead them to graduation by the hand. That depends on the competitivity of the program, some unis are so lucky they can only accept the best of the best from the program so mayassume the problem is only in the advisor.
@JobHunter69 Academia and industry aren't the same thing. Some cynics here may think that all a professor wants from a student is to work them hard for publications and then abandon them, but I don't find that the norm.
In addition to the existing answers: inevitably, PhD students tend to require more input from their supervisor in the first half of their programme, and then gradually become increasingly independent towards the end. Conversely, outputs and impact tend to be low in the first half of a programme, and increase towards the end. A student who leaves half-way