Camille Pickren, is on a career trajectory since joining
Dish in 2016 moving from entry level to senior to lead data scientist to now
the Business Operations Manager of Data Science. Her work takes her into the
deep mechanics of data targeting for marketing, programming and sales.
“As a team, we are in charge of all of the propensity
modeling. So if they decide ‘this is the kind of audience we want to reach,’ we
run a model and score all of the households with most presence of those most
likely to want to watch your show,” she explained. But in addition to that, her
team also does direct targeting and various post campaign reports. “We have a custom
linear product, addressable reports and most recently, the Reach Booster,’ that
analyzes how a campaign did in linear and then adding addressable ads to those
homes that missed the linear messaging.
Introducing Reach Booster
One of the
challenges is that no matter how much you may spend in a linear campaign, you
always tend to max out at 70% of the audience, leaving a substantial audience
still to be exposed to the messaging with the right amount of frequency. How to
best reach this additional 30%? “The idea was to find these linear ad schedules
and find everybody who had seen the ad and then target those who didn’t.” This
elegant solution enables advertisers to not only maximize their reach but also
attain the correct amount of frequency. “We’ve run several beta campaigns at
this point and they have been really successful,” she added, “reaching people
who are not seeing linear campaigns.” Using Reach Booster, Pickren was able to
increase reach to over 90%.
Finding the Best Data Points
In a world
awash with granular first party data, we are faced with an embarrassment of
data riches in trying to choose which sets are best for a particular sales
campaign. “We have thousands,” she began, “People tend to stick with the
traditional ways that linear was designed. But I have seen that slowly starting
to shift.” So while possibly starting with age and gender, “people are starting
to realize how many data points we have,” and are drilling deeper, leading to a
far better advanced target. A recent
request started as, “we want men 18-45 who made over $75k and also had more
than an acre of land,” resulting in a very adequate sample size of 200,000
households for an advertiser of farming equipment. This highly targeted data approach
insured that there would be as little waste as possible. “There are so many
datasets that it’s a little overwhelming for people to decide which things they
want and don’t want,” she noted, but they are getting more sophisticated over
time.
Insuring Privacy
When asked
about the biggest challenges facing the data business today, the CCPA looms
large in both privacy and licensing. But, “Dish, very early on, formed an
entire board – a privacy team – dedicated to privacy compliance. We are very
protective of our data, more so than industry standard, so we haven’t had to
change a lot,” to comply with the new privacy legislations in the U.S. and
across the world, she explained. “So far what we have done is put into place
process so that if someone calls and says, ‘I don’t want to be targeted’ we are
ready for that.” Further, none of the data points that Pickren can access is
connected to a name or an address. It is all anonymized, even internally.
“Different teams have access to different things and no one person can put it
all together.”
Education
This respect
for the customer coupled with both linear and addressable advertising opportunities
for advertisers places Dish in a strong position within the media ecosystem. And
in addition to external sales efforts, Dish also focuses internally, educating the
sales executives on all of the different choices of data available and how the
data can be best used for an advertising campaign. “We have a lot of new people
coming into the company and they may not know what data we have available,” she
explained, “So it takes time.” Balancing both external and internal
constituencies is both an art and a data science for Pickren.
This article first appeared in www.MediaVillage.com
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