The data used for marketing a generation ago, including information like point-of-sale transaction data, responses to direct mail campaigns, and coupon redemption numbers, was incredibly limited compared to the data available today. With big data, marketing has experienced tremendous innovation. But simply having and processing big data doesn’t automatically lead to better marketing.
Big data is a crucial ingredient in today’s marketing mix,
but with big data, marketing improvements aren’t automatic. It’s the insights
that marketing professionals gain from big data that drive better marketing
decisions. Results can include better customer engagement, better customer
retention, and optimized marketing performance.
Why Data Must Be Clean
Outdated, inaccurate, or duplicated data won’t drive optimal
marketing decisions. Big data about customers and markets is constantly
changing, so if you’re using last year’s data, you may not have a complete,
360-degree view of your customer. When data is inaccurate, leads are harder to
track and nurture, and insights may be flawed. The data on which you base your
big data marketing strategy must be accurate, up-to-date, as complete as
possible, and should not contain duplicate entries. Clean data results in
better marketing decisions.
Why Data Must Be Integrated
To market most effectively, you need a comprehensive view of
who your customers are. But you can’t develop a comprehensive customer view if
your data is locked up in silos, with sales having one set of data, marketing
having another, R&D having yet another, with no communication or
integration among the data sets. When different teams are working with separate
sets of data, they’re basing their insights and decisions on incomplete
information. One aspect of “clean data” is its integration. This is the only
way to develop the 360-degree customer view that all teams need to optimize
their strategies.
What Data Cleansing Accomplishes
Data cleansing may be done before or after it’s in your
database, though it is best if data is cleansed before being entered into a
database. What data cleansing does is remove errors and resolve
inconsistencies. The process involves identifying duplicate, incomplete, or
mislabelled data elements, and then removing duplicates, appending incomplete
data where possible, and deleting or correcting inaccurate data. With a robust
data cleansing process, you can be confident that the information on which
decisions are based is sound, and that the outcomes will serve business
processes better. Does it work? Absolutely. “Dirty” data can reduce lead
conversions, and total costs can be high: $83 for every 100 records in a
database.
Data Cleansing Isn’t a One-Time Process
Big data is constantly in flux, and around 2% of information
in a marketing leads database goes stale every month. By the end of a year, a
major chunk of a prospect database can be stale. Data cleansing is like tending
a garden in that it must be done regularly to ensure that the “produce” is as
strong and healthy as possible. Continuous cleansing of data is necessary for
accuracy and timeliness, and for ensuring that every department has access to
clean, merged, comprehensive data. You wouldn’t use old or stale ingredients to
make Thanksgiving dinner, and by the same token, you shouldn’t use old or stale
data to make important marketing decisions.
Conclusion
With big data, marketing can be taken to the next level of
effectiveness. Big data marketing requires that the ingredients – the data –
that go into making decisions be as high-quality as possible. Data cleansing
accomplishes this by removing or correcting inaccurate data, getting rid of
duplicate data, and filling in or appending data to create an increasingly
accurate and comprehensive view of the customer. The result: more effective marketing
and higher lead conversions.
Article From: www.reachforce.com
