It's not easy to predict the best time to call prospects.
Was helping a portfolio co. this morning and I dug in to what research says.. A thread π ππ
1/ But did you know that the best time of day to cold call a prospect is between 4:00 pm and 5:00 pm in their local time?
2/ During that window, most people are wrapping up their day, and thus are more open to disruptions than at other times. They're also likely to be at their desks.
3/ The worst times? Between 7:00 am to 10:00 am. As for the best day for sales calls, a study by Gong revealed that Wednesday and Thursday remain the best days of the week to call your leads.
4/ The reasoning behind this is that on Monday, buyers are still busy planning their workweek as they transition from weekend to work mode. And on Friday, they're already gearing up for the weekend and are less likely to be interested in talking with a salesperson.
5/ But regardless of the best and worst days to make a sales call, response time is everything.
6/ Case in point: 78% of buyers purchase from the first company to respond to their inquiry. That means the faster you can respond to an inbound lead, the better.
7/ Furthermore, sales teams that donβt respond within 5 minutes experience a lead qualification decrease of 80%
What times work best for you? (end)
β’ β’ β’
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Web3 is hot π₯π₯ and everyone has an opinion, and in the metaverse, there are wars raging: VCs vc @jack@levie@pmarca blocks anyone web3 critics etc..
(1) So at @Twitter university, I started learning and compiled a opinions, analysis and resources mega-thread of threads πππ
@jack@levie@pmarca@Twitter (2) Classic VC argument from @cdixon > Good painting of a future, some broad brushes, kumbaya, train is coming, get FOMO, etc.:
What did I learn from organizations getting started in #AI?
They start by not building, but by asking questions. (a thread..) (1/10)
What problems exist that needs to be solved?
Why can't this problem be solved by traditional software, Why do we need #MachineLearning ?(2/10)
The answer belongs to one in 4 categories:
Category 1) Need to process a lot of data and is not possible for traditional data analytics/BI methods to scale. Because you have to make many micro-judgements on pieces of information and you want to speed up decision making (3/10)