Sunday, 22 November 2015

No PhD in Data Science? No Worries — It Just Got a Whole Lot Easier

Data science gets a lot of attention these days—and for good reason. The insights it provides enable businesses to better understand customer behavior, then leverage automation to act on the intelligence and dynamically deliver the personalized journeys every customer expects. While the benefits are clear, leveraging cutting-edge methods to engage customers has remained beyond the reach of all but the largest of companies.

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