Right-Time Retail Part 3
- by David Dorf
This is part three of the three-part series. Read Part 1 and Part 2 first.
Normal
0
false
false
false
EN-US
X-NONE
X-NONE
/* Style Definitions */
table.MsoNormalTable
{mso-style-name:"Table Normal";
mso-tstyle-rowband-size:0;
mso-tstyle-colband-size:0;
mso-style-noshow:yes;
mso-style-priority:99;
mso-style-qformat:yes;
mso-style-parent:"";
mso-padding-alt:0in 5.4pt 0in 5.4pt;
mso-para-margin-top:0in;
mso-para-margin-right:0in;
mso-para-margin-bottom:10.0pt;
mso-para-margin-left:0in;
line-height:115%;
mso-pagination:widow-orphan;
font-size:11.0pt;
font-family:"Calibri","sans-serif";
mso-ascii-font-family:Calibri;
mso-ascii-theme-font:minor-latin;
mso-fareast-font-family:"Times New Roman";
mso-fareast-theme-font:minor-fareast;
mso-hansi-font-family:Calibri;
mso-hansi-theme-font:minor-latin;
mso-bidi-font-family:"Times New Roman";
mso-bidi-theme-font:minor-bidi;}
Right-Time Marketing
Real-time isn’t just about executing faster; it extends to
interactions with customers as well. As
an industry, we’ve spent many years analyzing all the data that’s been
collected. Yes, that data has been
invaluable in helping us make better decisions like where to open new stores,
how to assort those stores, and how to price our products. But the recent advances in technology are now
making it possible to analyze and deliver that data very quickly… fast enough
to impact a potential sale in near real-time. Let me give you two examples.
Salesmen in car dealerships get pretty good at sizing people
up. When a potential customer walks in
the door, it doesn’t take long for the salesman to figure out the revenue at
stake. Is this person a real buyer, or
just looking for a fun test drive? Will
this person buy today or three months from now? Will this person opt for the expensive packages, or go bare bones? While the salesman certainly asks some
leading questions, much of information is discerned through body language. But body language doesn’t translate very well
over the web.
Eloqua, which was acquired by Oracle earlier this year,
reads internet body language. By
tracking the behavior of the people visiting your web site, Eloqua categorizes
visitors based on their propensity to buy. While Eloqua’s roots have been in B2B, we’ve been looking at leveraging
the technology with ATG to target B2C. Knowing
what sites were previously visited, how often the customer has been to your
site recently, and how long they’ve spent searching can help understand where
the customer is in their purchase journey. And knowing that bit of information may be enough to help close the deal
with a real-time offer, follow-up email, or online customer service pop-up.
This isn’t so different from the days gone by when the clerk
behind the counter of the corner store noticed you were lingering in a
particular aisle, so he walked over to help you compare two products and close
the sale. You appreciated the personalized
service, and he knew the value of the long-term relationship.
Move that same concept into the digital world and you have
Oracle’s CX Suite, a cloud-based offering of end-to-end customer experience
tools, assembled primarily from acquisitions. Those tools are Oracle Marketing (Eloqua), Oracle Commerce (ATG,
Endeca), Oracle Sales (Oracle CRM On Demand), Oracle Service (RightNow), Oracle
Social (Collective Intellect, Vitrue, Involver), and Oracle Content (Fatwire). We are providing the glue that binds the CIO
and CMO together to unleash synergies that drive the top-line higher, and by
virtue of the cloud-approach, keep costs at bay.
My second example of real-time marketing takes place in the
store but leverages the concepts of Web marketing. In 1962 the decline of personalized service in
retail began. Anyone know the
significance of that year? That’s when
Target, K-Mart, and Walmart each opened their first stores, and over the
succeeding years the industry chose scale over personal service. No longer were you known as “Jane with the snotty
kid so make sure we check her out fast,” but you suddenly became “time-starved
female age 20-30 with kids.” I’m not
saying that was a bad thing – it was the right thing for our industry at the
time, and it enabled a huge amount of growth, cheaper prices, and more variety
of products. But scale alone is no
longer good enough. Today’s
sophisticated consumer demands scale, experience, and personal attention.
To some extent we’ve delivered that on websites via the
magic of cookies, your willingness to log in, and sophisticated data
analytics. What store manager wouldn’t
love a report detailing all the visitors to his store, where they came from,
and which products that examined? People
trackers are getting more sophisticated, incorporating infrared, video
analytics, and even face recognition. (Next time you walk in front on a mannequin, don’t be surprised if it’s
looking back.) But the ultimate
marketing conduit is the mobile phone. Since each mobile phone emits a unique number on WiFi networks, it
becomes the cookie of the physical world. Assuming congress keeps privacy safeguards reasonable, we’ll have a
win-win situation for both retailers and consumers. Retailers get to know more about the
consumer’s purchase journey, and consumers get higher levels of service with
the retailer.
When I call my bank, a couple things happen before the call
is connected. A reverse look-up on my
phone number identifies me so my accounts can be retrieved from Siebel
CRM. Then the system anticipates why I’m
calling based on recent transactions. In
this example, it sees that I was just charged a foreign currency fee, so it
assumes that’s the reason I’m calling. It puts all the relevant information on the customer service rep’s screen
as it connects the call. When I complain
about the fee, the rep immediately sees I’m a great customer and I travel lots,
so she suggests switching me to their traveler’s card that doesn’t have foreign
transaction fees.
That technology is powered by a product called Oracle Real-Time
Decisions, a rules engine built to execute very quickly, basically in the time
it takes the phone to ring once. So
let’s combine the power of that product with our new-found mobile cookie and
provide contextual customer interactions in real-time.
Our first opportunity comes when a customer crosses a
pre-defined geo-fence, typically a boundary around the store. Context is the key to our interaction: that’s
the customer (known or anonymous), the time of day and day of week, and
location. Thomas near the downtown store
on a Wednesday at noon means he’s heading to lunch. If he were near the mall location on a
Saturday morning, that’s a completely different context. But on his way to lunch, we’ll let Thomas
know that we’ve got a new shipment of ASICS running shoes on display with a
simple text message.
We used the context to look-up Thomas’ past purchases and
understood he was an avid runner. We
used the fact that this was lunchtime to select the type of message, in this
case an informational message instead of an offer. Thomas enters the store, phone in hand, and
walks to the shoe department. He scans
one of the new ASICS shoes using the convenient QR Codes we provided on the
shelf-tags, but then he starts scanning low-end Nikes. Each scan is another opportunity to both
learn from Thomas and potentially interact via another message. Since he historically buys low-end Nikes and
keeps scanning them, he’s likely falling back into his old ways. Our marketing rules are currently set to move
loyal customer to higher margin products. We could have set the dials to increase visit frequency, move
overstocked items, increase basket size, or many other settings, but today we
are trying to move Thomas to higher-margin products.
We send Thomas another text message, this time it’s a
personalized offer for 10% off ASICS good for 24 hours. Offering him a discount on Nikes would be
throwing margin away since he buys those anyway. We are using our marketing dollars to change
behavior that increases the long-term value of Thomas. He decides to buy the ASICS and scans the
discount code on his phone at checkout.
Checkout is yet another opportunity to interact with Thomas,
so the transaction is sent back to Oracle RTD for evaluation. Since Thomas didn’t buy anything with the
shoes, we’ll print a bounce-back coupon on the receipt offering 30% off ASICS
socks if he returns within seven days. We have successfully started moving Thomas from low-margin to
high-margin products.
In both of these marketing scenarios, we are able to
leverage data in near real-time to decide how best to interact with the
customer and lead to an increase in the lifetime value of the customer. The key here is acting at the moment the
customer shows interest using the context of the situation. We aren’t pushing random products at
haphazard times. We are tailoring the
marketing to be very specific to this customer, and it’s the technology that
allows this to happen in near real-time.
Conclusion
As we enable more right-time integrations and interactions,
retailers will begin to offer increased service to their customers. Localized and personalized service at scale
will drive loyalty and lead to meaningful revenue growth for the retailers that
execute well. Our industry needs to
support Commerce Anywhere…and commerce anytime as well.
