As you know, we think about data differently from any other data vendor. In our last blog post, we talked about our views on data, list buying, and what to do with the list. Now, we’re breaking down that infographic. First up, we’re talking about how big of a list you actually need.
There’s typically three primary strategies when it comes to buying data:
We’ve broken down each strategy so you can see how efficient compared to how effective each one is:
1) “Big fat list”: High efficiency, low effectiveness
A list vendor tells you that you can get 10,000 contacts with 100% data quality guarantee! But are there even 10,000 contacts you want? And are there contacts being added that you don’t want?
Be aware of “volume discounts” with this strategy. If you’re given bad data (wrong titles, outdated numbers, etc.) is the vendor going to replace it? What’s the burden on you to prove it doesn’t work? Who in your organization is responsible for reporting that bad data and following up on the replacement or refund?
TLDR: You usually end up with junk data.
2) “Massive database”: Medium efficiency, medium effectiveness (high cost)
Best case scenario: you get access to a database specific to your industry so you’re not buying data that’s only relevant to someone else, but it’s only refreshed quarterly at best.
Worst case scenario: you are sold on the “XX million B2B records” they have access to and have no clue how many are in your target market.
Efficiency seems high for this strategy, but the average accuracy promised is usually 70-80%. You have to identify targets, pull them into your database, and hope they’ve been recently validated. You also must think about who is responsible to run searches and who gives license access, doles out credits, backfills, cleans up, and gets refunds or credits back.
We just went through this exercise with a client who had a list they had bought. We found that, of 1000 records, 10% had bad emails and 10% were duplicate titles or had moved into different roles. It’s hard to get someone’s attention these days, so why spend any effort on the wrong person?
3) “Barrel scraping”: Low efficiency, medium effectiveness
So you read a blog about growth hacking and find out you can hire people offshore to “scrape the internet” for leads. Sounds cool! Actually, this is the most tedious strategy to either do yourself or to manage using other resources. Plus, it takes a lot longer and there’s rarely any guarantee as to the accuracy of the information.
While it sounds like this is least expensive way to build a list, that’s just the upfront cost – it doesn’t include the time you’ll take to replace the bad data when an email bounces or a phone number doesn’t work. It certainly doesn’t take into account all the attempts to reach someone who’s information hasn’t been validated.
None of these three common strategies approach the high efficiency and effectiveness required to truly enable sales professionals to spend all their time professionally selling. That’s why we came up with our own way to handle data:
4) DATA ON-DEMAND: High efficiency, high effectiveness
You simply buy only the validated data you need, when you need it, with a plan for backfill.
You can’t afford the time to source and validate it, but somebody has to do it. Since this process has a higher up front cost per record, you should only buy what you’ll actually take action on. That means knowing your Math of Sales.
If you want to speak with someone on our team and get your free Math of Sales worksheet to help you understand how much data you actually need, click here.
We’d love to hear your thoughts about list buying and what strategies you use. Do you use one of the three primary strategies discussed above? Let us know in the comments below!