By Bob Fetter, SVP of Pluris
Marketing
Everyone is
talking about big data and judging from my social streams and much of the
media coverage, I’m afraid many don’t have a clear understanding about what it
is, and if big data is in fact, what they are dealing with. The explosion of
data has everyone thinking- “this must be a big data problem” but sadly, a few
million emails, a couple thousand social messages and a customer data base,
isn’t big data. It may be a lot of data to manage and a headache, but how does
the average CMO or CTO build a strategy around handling their data if they
prescribing it inaccurately?
Big data is
best defined by the three dimensions of Volume, Velocity and Variety. It is data with high volume, that is
continually generated with great speed, and has lots of different types of
data, be they structured, semi-structured or unstructured. It’s generated out of sensors, games,
mobile devices, set top boxes, social media sites, video players and many other
places. The question for marketers
is: what do you need to know and
do about it right now?
Identify, Hypothesize, and Analyze
First off,
you have to understand if you have a big data problem. If you don’t think you
do, then you likely don’t. Big
data is visceral; it flows all around your business in ways that you can
feel. A client recently told me that
he couldn’t get on top of his data because conversely it was on top of
him. And this was after opening a
relatively simplistic new source of approximately 600mm well-structured data
records per month. Is there data
around you that you intuitively feel you can use, but can’t get a handle
on? If this is the case, you
likely have a big data opportunity.
Once you
have identified the opportunity, develop one or more hypotheses around the use
and value of data. For example, I
have been continually amazed that marketers do not typically have access to the
complete digital view of their customers. They are sending them emails, and posting
on Facebook with links back to their core digital content. Why can’t we understand the complete
path from an act of opening an email, to following links on a website, to an
actual purchase, be it in store or on the web? It involves big data, generated out of the raw web logs of
your digital content.
Suppose you
are a retailer that has 100,000 SKUs within your inventory at any point in
time. What SKUs were in the email
that caused the open and click behavior?
Once on the website, where did that shopper go next – what other
products or product class areas did they look at? What were the price points of the products viewed? Did that visit result in a conversion,
and did it somehow relate to the initial SKU contained within the email? Or, was it the promotion (e.g. free
shipping) that caused the observed behavior? By collecting and analyzing this behavior across all of your
emails, all of your subscribers and all website visits, would patterns of
behavior emerge that you can leverage to drive new marketing and engagement tactics?
Keep ‘Projects’ at Bay
Now that you have one or more hypotheses to test, it’s time
to take advantage of the rapidly emerging technologies that are out there. Just
don’t turn it into a “project.” If
you use your traditional go-to resources (i.e. internal IT), it’s likely that
they will have neither the time nor expertise to test your hypothesis
appropriately. In addition,
traditional development cycles do not apply here. Even with a six month project cycle, the opportunity may be
lost as a whole new set of merchandise, economic conditions, or new data
sources arise. You therefore need
to be able to test your hypothesis quickly and cost effectively.
If you can’t find the resources in-house, there are hundreds
of emerging companies that provide both big data and big data analysis
capabilities in the cloud as ‘Software as a Service’ (Saas). At this point you don’t need to
understand all of the underlying technologies you are reading about, just that
they are useful for rapidly consolidating your big data into forms more
accessible for analysis and hypothesis testing. Your choice of companies and
methods are dictated by where you face bottlenecks. Do you have the analytic horsepower on your staff to work
with the data? Then find data
mobilization experts. Do you have
ready access to developers skilled in advanced scripting and data movement but
no statistical bandwidth? Then
it’s time to move in a different direction. If you are severely constrained across many fronts, then a
broader full service big data provider might be in order. The point is that there are readily
available outsourced resources to quickly test your hypothesis, provided you
can get the data to them.
Lock and Load
Once your big data hypothesis is tested and proven, then you
have the ammunition necessary to institutionalize your new process, based on
your marketing ROI. This
institutionalization will involve a mix of internal and outsourced resources. But even before heading down this path,
make sure you have the “small” data right, as I posted
a few months ago.
Big data is here to stay, providing a new task for the
already challenged marketer to take on.
But the promise of real ROI which moves the needle in consumer engagement
is too great to ignore. Big data
allows us to take a broader view than the traditional “one campaign at a time” optimization,
and it allows us to adapt quickly to underlying changes. Several key challenges facing retailers
can be solved, like promotion, offer and price optimization, or even
merchandise optimization to a specific store location. The data is there, we just need to
harness and use it to reap its rewards.
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