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Data and the Now - for Technology Sellers

Marketers are becoming aware of the importance of timeliness. It is everything, and it will always be.

Finding what is the email or phone number of your friend at the moment, is quite easy. It's a little more difficult when it is 100 invitees. So imagine how it gets when we have to collate the correct information of 32 million people.

And it's not just multi-channel contacts, but actually inviting them to our database. If they don't wanna give out, they don't. And it's about millions of people from certain industries, and hundreds of thousands from others.

There are various ways to do this. However, to stay in business, you've gotta do it legally. We have about 400 calls being made from our floors - by each person - in a team of 150. That's 60,000 calls a day, 12,000,000 calls a month - AT LEAST! These are the invitation calls, and there's no wine, feast, or even an occasion.

So why do they care?

Technology sellers in every industrial city of the world want to find our invitees wherever they can. Because that will bring technology sellers the money they need. But why should the buyer of some odd technology give out their details to us for you? Do they really hope to be found by you? No.

They expect you to be a few miles from their doorsteps. Whether it is for signing a software contract at a coffee shop, or a live demo at your home. And we are giving you their details, because we know what they are looking for - we've asked them, and verified the fact that they will invest in the technology you sell.

There are many problems with this collection as you would believe. The most dangerous part is doubting whether the names and contacts you collect are true and solicited. There can be no doubt, and there can be no mistake - no spelling errors, missing ZIP codes, etc.

That's why NOW is important. It doesn't really matter whether you have 30 contacts or 32 million. They have to be working. And we're talking contacts of people who really want to buy from you. That's why big data often fails in the real world. It requires excellence!




   

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