The days of spray-and-pray advertising are dead
The
days of spray-and-pray advertising are dead. They have been for a long time.
These days, audience targeting is a critical part of
any marketing campaign. Most marketers use Big Data—the decade's sexiest
buzzword—to target prospects.
The bad news is that Big Data is worthless unless it's of the
highest quality.
Unfortunately, most ad campaigns are fueled by irrelevant,
untrustworthy data. Bad-quality data costs companies on average nearly $13
million per year, according to a Gartner survey.
If you don't have high-quality data, you're in a lose-lose
situation: You lose out on potential customers because of mistargeted ads, and
you put yourself at risk financially, legally, and reputationally.
To address that issue, before launching campaigns you should
ensure the accuracy and quality of any data that you are using.
Easier said than done, right? But there are ways to verify data
sets and ensure that the data you amass in the future is trustworthy and
accurate.
Conduct a data audit of your current assets
- The data has been collected with the appropriate consent.
- It abides by privacy regulation requirements.
- The sources and methods of data collection are transparent.
- The process for producing the resulting data follows industry best-practices.
1. Identify and eliminate duplicates
2. Check for out-of-date data
3. Look for incomplete records
Prevent
bad data in the first place
1. Standardize your data auditing process
2. Set up ongoing data monitoring
3. Rinse and repeat
Don't
let bad data ruin your ads
You might not be sure of the quality of your data right now, but
it's possible to introduce processes that will help you find out exactly what
you're dealing with. Possible, and necessary: data verification and hygiene
should be a routine practice.
To start, conduct a thorough audit of all internally and
externally sourced data sets. That includes data that you are directly
collecting as well as data that you license from other partners.
Nobody likes the word "audit," but it's a necessary
part of your data verification process. Simply put, you need to confirm that
your data is legally OK.
Use internal teams or work with a partner that specializes in
data process and policy verification to confirm that...
Some of your data might not be up to snuff, but it's better to
find that out now than during a lawsuit later.
When you have a better understanding of the processes and
policies used to produce your data assets, you can conduct a risk assessment of
your data sets. That will then help you decide which data sets to continue
using and which to stop using because of potential compliance issues or quality
risks.
After your audit, it's time to review and assess the data
itself. When reviewing and cleaning your data, there are a few key problems to
look for.
Duplicate data can come from a variety of places: Data
migration, manual data entry, third-party connectors, data exchanges, and batch
imports can all cause duplicate entries.
Having multiple records for the same data point means you're
paying extra for the storage, and you risk misinterpreting your results. You're
also sending redundant messages to your prospects.
Data is not like wine or cheese. It doesn't age well at all. In
fact, the older the data, the less useful it is.
For instance, you may be targeting a prospect who clicked on
your ad a year ago, but who no longer works at the company you have in your
records. Or you may be relying on intent data from a partner that was
based on shopping behaviors the person exhibited six months ago while your products
have a much shorter purchase cycle.
Incomplete records are especially likely if you have any kind of
manual data input in your process.
Imagine you convert 30% of your prospects who come to you
through Google Ads, but for half of those conversions you don't have the
marketing channel recorded as a consequence of a technical error. When it's
time to pull together a marketing report, you might mistakenly believe Google
Ads aren't very effective. You could lose the opportunity to double-down on a
winning strategy, and you might instead mistakenly invest in a less effective
channel.
When you have a handle on the scope of the data cleansing
problem, you can decide whether it's small enough to fix manually or whether
it's worth investing in technology to help.
If you're just a small company, it may be worth your time to
spend a few days hunting through your CRM to ensure your records are complete,
and maybe spend some time on LinkedIn to update your data. If you have too much
data to reasonably comb through manually, you can look to automated technology
or services that could do it for you instead.
Ideally, you won't find yourself in the position of having to go
back and verify data sets after you have been using them. As they say, an ounce
of prevention is worth a pound of cure.
Here are three tactics that can help you avoid the issue of
low-quality data altogether.
Establish the criteria that you will rely on to select data
sources up front, and establish a process—internally or with an audit
partner—for verifying compliance with your criteria. Such verification must be
conducted before working with new data partners or introducing new data
collection methods yourself. It needs to become a standard operating procedure
to ensure everyone knows your best-practices and is evaluating data sets
consistently.
Despite your best-practices, bad data can and will creep in.
Even if your data collection and sourcing technique is flawless, age will
render your records less valuable. Data partners can modify their collecting
techniques or methodologies, which can then have an impact on your performance
from using that data.
Instead of waiting for data quality to become an issue, set up
regular monitoring of your data, either automated or manual. Also, conduct
regular re-audits of your data partners to verify what has or has not changed
in their processes.
If you repeat those two steps, you'll have a clean, robust,
low-risk data set you can rely on for your marketing campaigns.
In a perfect world, ideal prospects would see the most
persuasive ad at exactly the right time, and they'd convert.
That perfect world is not so far-fetched: Data can help you
accomplish that with ease—if it's good-quality data.
Consider this situation: You source data from a partner that did
not provide a clear opt-in during the original data collection. That partner
also wasn't transparent with the user about how the collected data would be
used. You try to contact the prospect, but you're met with annoyance and
frustration because, of course, he or she never consented to being contacted by
you in the first place. That prospect does not turn into a lead, which
obviously is a waste of your time and money. Worse, you could face potential
compliance issues as a result of your using data that was collected
noncompliantly.
Now imagine this scenario: A Google ad reaches your ideal
prospect, who sees it and, upon being presented with a clear opt-in, signs up
for your mailing list. Your sales team speaks with that person and knows
exactly what the prospect's needs are thanks to the clear consent and clean
data about that prospect. The prospect converts, and your marketing team knows
that Google Ads are a great way forward thanks to clean data collection and
clear consent from the prospect to be contacted by you. Big Data for the win!
By rigorously validating data sources, cleansing current data,
and setting up procedures to avoid poor data collection risks in the future,
your ads will increase in both effectiveness and ROI.
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If you need help with your email, web site, video, or other presentation to promote your company, product, or service, please give me a call at 440-519-1500 or email me at john@x2media.us
Until next month. . . .remember. "you don't get a 2nd chance to make a 1st impression." Always make it a good one!!
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