According to
Leslie Fine, VP of Data and Analytics at Salesforce, that’s all in the process
of changing. Here’s what the data expert had to say about the future of
data-driven apps, and how they are transforming customer engagement for
companies of all shapes and sizes.
Why is data
science so critical to marketers?
Every
marketer wants to create 1-to-1 relationships, but that can be incredibly
difficult to do at scale. Data science is the key to unlocking customer
behavior, identifying trends, then automating reactions to the insights
discovered. It empowers marketers to predict customer behavior and deliver
relevant and personal experiences with every interaction. It’s a winning
formula for both the consumer and the business. Customers get the content they
want at just the right time, and it allows marketers to move from impersonal transactions
to relationship building.
I’m sure
every marketer would love to have a data scientist on staff. What’s the
alternative?
At the turn
of the century, marketers had to rely on gut instinct. Then came more informed
analytics and business apps. Today, it’s all about automated and adaptive
decisions from data-driven apps—they put the power of data science in the hands
of marketers. The state of the art is rapidly evolving to deep technology with
a marketer-friendly interface. Our job as data scientists is to bridge this
gap.
Why are
traditional business apps like ERP no longer sufficient?
They’re
complex to use, require entire teams to administer them, and valuable time is
wasted on the painstaking task of data entry. On top of all this, the apps aren't
marketer friendly --the data doesn't inform marketers what to do next and
requires technically skilled resources to make it actionable. Getting maximum
value requires data experts to segment and target customers.
Meanwhile,
marketers want to build stronger, longer-lasting relationships with customers.
To do this, they must understand customer interactions across the entire
ecosystem, identify customer behaviors, and accurately anticipate what they
might do next. Data-driven apps using machine-learning algorithms to uncover
models are the answer.
Are
marketers leading the way, or following the customer?
I believe
it’s a reaction to how customers have evolved. We all know that today’s
customers have more options and control than ever—much of this was driven by
the mobile and social revolution. Customers are more connected than ever,
creating massive digital exhaust and super high expectations. They expect
brands to know who they are, where they are in their journey, and whether they
realize it or not, what content or offer they want next.
How do
data-driven apps solve this?
They
leverage the wealth of data each business collects to tell them exactly what to
do next to provide the most value to customers. They automate analytics to
unveil digital intelligence, enabling marketers to deliver the right message or
offer in real time. It’s like having millions of analysts pouring over your
data to identify patterns, only it’s faster, cheaper and way more accurate.
Think of
using a GPS in your car to navigate, as opposed to a map. Do maps let you
specify your destination? Do they automatically compute the fastest route or
suggest alternatives when traffic patterns change?
But when you
use a GPS, you just input your destination and start driving. The GPS does the
hard work of processing complex data about traffic and distances to provide
turn-by-turn directions at just the right moment. This is how we think of
data-driven marketing. You give us the goal, and we will help you create the
customer journey to drive to that goal.
How are
marketers are using data to drive their business?
Businesses
are harnessing the power of data in all kinds of ways. For example, Room &
Board uses Marketing Cloud to create digital experiences reflective of the ways
their customers use the Web alongside retail stores and call centers. The
company started uploading all of its customer sales history and data to the
cloud in 2009. Years of data about what
pieces of furniture go together, what styles complement one another, and what
products customers tend to view and purchase in groups informs recommendations
made on the website and in personalized email campaigns.
What types
of results do they see?
Customers
who engage with Room & Board’s recommendations place Web orders with 40%
higher average values than those who don’t. When customers view those
recommendations before coming into the store, the average order value shoots up
60%. In their first year of doing this, they realized a 2,900% return on
investment.
How can
marketers gain deeper insights into the health of their audiences?
Historically,
they’ve had to rely on explicit indicators like transactional data, web
analytics, engagement history or third-party data. While these metrics are
incredibly valuable, they don’t reflect the entire customer story. The
challenge is integrating all pertinent indicators—then automating engagement
strategies based on findings.
Marketing
Cloud has solved these issues with Predictive Journeys. For example, our
Predictive Scores dashboards reveal an engagement score for each member. With
this, an email marketer could understand a customer’s likelihood to open or
click and email, stay subscribed, or even make a purchase. They can then use
this intelligence to deploy a re-engagement campaign. It not only helps them to
monitor the health of each subscriber, but also their entire member base in
real-time. They can uncover new insights, driven by algorithms and machine
learning, to identify who is likely to engage and what attributes predict
engagement.
That sounds
simple enough if you’re targeting one subscriber at a time. How does it scale?
That’s where
Predictive Audiences comes in. It enables marketers to go beyond analytics to
take action. They can develop segments by pairing Predictive Scores with
thousands of other attributes, then deploy tailored journeys across channels
like email, mobile, social, the web or ads. The audiences they create are
continuously updated in real-time—they’re always working with the latest data.
New customers that meet the criteria come in, and others out, depending on
their actions and the attributes associated with the segment.
How can
marketers get started?
Predictive
Journeys are core to Marketing Cloud. As part of each customer's profile,
marketers can leverage Predictive Scores and Predictive Audiences to filter and
segment their audiences, along with any other attributes they choose, based on
their specific goal (open, click, unsubscribe, purchase). And once the segments
are created, the machine learning takes over again to adjust a customer’s
journey based on their engagement likelihood.
Source: salesforce
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