Ah, spring: The season that awakens the desire to clean and organize … in other words, to do your spring cleaning.
Why do we have “spring cleaning” and not “fall cleaning” or “summer cleaning?”
Turns out there’s a reason.
The term spring cleaning comes from a time when homes were buttoned up tight in the winter to keep the heat from the fireplace inside. When you burn fires all winter long inside an airtight home soot builds up on walls, rugs, furniture and, well, anything and everything. Yuk!
The arrival of spring meant windows and doors could be flung open while the house was scrubbed clean of the accumulated grime.
Houses aren’t the only thing that accumulate grime.
Your valuable prospect and customer databases also accumulate grime that, if left unattended, can suck the oxygen out of your sales and marketing campaigns.
Here’s the thing; most of us no longer need to worry about soot accumulating in our homes. Why? Because we have modern heating systems.
Cleaning data automatically
What if we could modernize our approach to data as well so that instead of needing to spring clean, we’re able to keep the “soot” from polluting our data in the first place?
If you’re not convinced about the high cost of bad data, you will be interested to know that it costs $1 to verify a record as it’s entered, $10 to scrub and cleanse it later, and $100 if nothing is done.
Read it: Dirty Data Done Dirt Cheap
Why cleanse your data?
There are 5 types of “soot” that a modern data system must account for, if you’d like to keep your data clean on a continuous basis.
- Duplicates: You have multiple Jonny Quests
- Garbage: Junk emails and made-up titles in web-form data
- Expired or Invalid: Jonny Quest is now at a different company with a different email.
- Irregularities: You have a Jonny Quest, a John Quest, and a Jon Quest – all are the same person.
- Incompletes: There is no email or phone associated with Jonny Quest
If you’re wondering, “What’s the harm in holding off and doing a once-a-year spring data-cleaning?” – I can give you a one-word answer: MONEY. Higher costs, lost revenue, and longer sales cycles.
Dirty data = higher costs
Without cleansed data, marketing emails go to dead records and get caught in spam traps and honeypots. Email deliverability rates drop, and your organization ends up on a blacklist. And in the meantime, you’re paying your Marketing Automation Tool (probably by the record) to send to a much larger list than you need to.
Incomplete lead records make it difficult for marketing automation systems to segment properly, which means emails end up with irrelevant, ineffective messaging – demolishing open, click-through, and response rates.
Given that prospect and customer databases double every 12-18 months (much like the dust bunnies under your bed) you can imagine the impact it will have on your overall costs if you wait until spring to do a one-time cleaning!
Dirty data = lost revenue
Can you generate revenue from someone that no longer works at the company you’re selling to? No!
Is it possible to sell to the new person that took his place? Yes!
If a salesperson knows of the change, they have a chance of generating revenue. If they don’t know about the change, they have no chance of generating revenue. It’s as simple as that.
Dirty data = lower productivity
Can you believe 37% of email addresses change annually?
As a result, sellers waste a great deal of time trying to find the new person, or the missing email, or the missing phone number.
They also waste time reaching out to leads that will likely never buy because they don’t have the power to make purchase decisions, or aren’t the right contact. Indeed, sales reps spend 1/3 to 1/2 of their cycles trying to reach contacts that are no longer in the same position – or have left the company entirely.
Besides the hard dollar costs associated with this wasted time, consider the significant opportunity cost. And down the line, sales reps experience burnout and turnover much faster when their rate of success is so low. The costs here are significant and far-reaching.
When leads contain bad prospect data, the sales cycle gets longer.
Sorry, folks: The annual spring cleaning of your database is not enough!
If you’re not scrubbing your data every 30 – 90 days, you’re looking at higher costs, longer sales cycles, and lower profitability. In addition, you won’t be able to measure the effectiveness of marketing and sales activities, so the dirty data cycle continues.
Here’s what you should be doing year-round to keep your data clean:
- De-dupe (or better yet, block duplicates from even being created)
- Verify email addresses (in real-time – and regularly)
- Leverage an intelligence provider to update your data on the latest job moves and email bounces
- Use data about prior engagement to drive outreach
- Append your data regularly with company and contact insights
No professional wants to slow the pace of revenue generation just to avoid a little dirty work. But most companies work with a volume of data that is difficult to clean manually – in the spring or any other season. That’s why it’s helpful to talk to a data provider with ongoing data cleansing.
When you talk to a data provider, ask them whether they have a modern data platform that keeps “soot” from accumulating in the first place.
Leave the spring cleaning for your closets. It doesn’t belong in the world of data.
Learn more about dirty data – and what to do about it: