Automating enterprise content By 2018, Gartner predicts that 20 percent of all business content will be authored by machines. Finding patterns in your data “AI is really good at automating those manual tasks people hate to do. Automate what you need to report on. Let’s say your company wants to own a market topic like ‘home and garden tips.’ Solutions such as MarketMuse can help identify all the angles you’d want to cover on that topic, showing you the core topics to tackle first and mapping keywords to different user intent profiles. Machine learning and AI can help you analyze the model of authority in this industry, showing you the coverage and related topics you need to cover. AI is helping to make personalization a reality for brands of all sizes. “A lot of personalization used by organizations is still quite simple. “For example, you can look at five types of blog posts and say if people are interested in this topic, they’ll likely want to read on this other topic too.” Machine learning can include much more data in their recommendations, looking at a user’s individual behavior across many different types of content such as reading case studies, clicking on an Instagram ad, opening emails, watching videos, and completing tasks in the product. How does machine learning work? You can find out more about customer data platforms in Gartner’s Hype Cycle for Digital Marketing and Advertising, 2016.
“I’ve read all your lyrics.”
“You’ve read all my lyrics.”
“My analysis shows that your major themes are that time passes
and love fades.”
“That sounds about right.”
“I have never known love.”
“Maybe we should write a song together.”
It’s clear: the marketing machines are here.
From
IBM’s Watson chatting with Bob Dylan (above) to
robot reporters churning out financial and sports articles, AI
is rewiring how marketers plan and execute campaigns.
But should your organization really invest in AI? Or is this
only a play for brands like IBM, Google, Facebook, Spotify, and
Amazon?
I wanted to learn more about the viable use cases of
AI—applications that can bring organizations value today, not in
five years time.
In this article, I share the five use cases that are gaining
traction across industries. These are based on Skype interviews
with leading AI companies, content marketing experts, and analyst
reports.
1. Robot reports
Hootsuite creates a lot of content. And we spend a lot of time
measuring how much traffic it drives, how many social shares we
get, and how much revenue it creates.
AI could obviously help to analyze our content performance. But
we’re not a consumer brand like Google, Spotify, or Amazon. So I
was skeptical about whether AI could deliver practical reporting
uses for an organization of our size.
Quill changed
my mind. In a few seconds, this machine spit out a Google Analytics
report. It was well-written, dug up some mobile data I hadn’t even
considered, and revealed what caused a revenue lift on a recent
advertising campaign.
I’ve worked in agencies and a large part of your time is spent
digging through analytics, presenting data, and summarizing what
campaigns are driving traffic. Quill delivered a comparable report
in a few minutes.
I’ve since set up Quill for our team. This saves us a few hours
a week. Not bad for a first introduction to using AI to automate
tasks. If you have a Google Analytics account, test it out free
here.
2. Automating enterprise content
By 2018,
Gartner predicts that 20 percent of all business content will
be authored by machines.
I wanted to find out more about Quill, so I reached out to
Narrative Science,
the company that makes the solution. Narrative Science helps
enterprises use natural language generation to analyze and tell
stories from their data.
“We hear all the time that content is king—but what’s really
helping many of our customers is that AI can help automate a lot of
high-quality content at scale,” Katy De Leon, Narrative Science’s
VP of marketing, told me via a Skype interview.
“This saves time for employees. But also delivers a better
customer experience as content is accurate and more relevant to
their stage in the customer journey.”
For example, a large organization might have thousands of
products. It’s always a struggle to make sure product descriptions
are up-to-date and optimized for search and mobile. “Automation
technologies can help your team write these descriptions and manage
of all this content,” says De Leon.
“When you put out one piece of communication like a blog post or
press release that is read by a million people, that is not the
best place for AI,” she says. “But when you put out millions of
pieces of communication that need to be personalized for each
person, that is where AI companies like us shine.”
A popular use case comes from Narrative Science’s financial
services customers. The platform can create individualized investor
reports for millions of clients, telling them how their bidding has
gone historically and offering specific advice for improving their
performance. Advisors can also use these reports to prep for calls
with clients.
3. Finding patterns in your data
“AI is really good at automating those manual tasks people hate
to do. But AI is more than an efficiency play,” Narrative Science’s
chief scientist Kris Hammond told me.
“Because AI can analyze datasets in seconds, the machines are
providing insights that are simply not humanly possible to find in
data,” Kris says.
For example, AI platforms can look at millions of transactions
and predict a customer who is about to churn. Or they can analyze
an enterprise sales cycle and predict which deals you’ll close in
the middle of a sales cycle by taking a look at historical
conditions.
“Data is a perfect match for the machines,” says Kris. “You
already have talented human analysts in your organization. But they
have higher value activities to do than manually look through rows
and rows of customer data. The machine can work faster,
understanding customer trends instantly.”
I asked Kris what organizations can do to get started with AI.
He offered three steps.
- Automate the things your team hates to do. These are the
tedious tasks at the bottom of your skillset. The machines can help
here. - Don’t automate your job. Automate what you need to report
on. Most people like their job but don’t enjoy constantly
creating reports about what they do and the value they create. You
can free up resources, creating more value instead of reporting on
what you did last week. - Look at the task and ask: am I making decisions that are
mechanical? If the answer is yes, then that task is a good
candidate for AI.
4. Reducing duplicate work across the enterprise
As AI is rewiring the way enterprises create and analyze
content, I wanted to know about the impact of all of this on SEO.
This led me to MarketMuse….
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