Shutterfly was facing challenges in 2017 with outdated legacy systems that made improving customer experiences costly and time-consuming. They wanted to upgrade their ecommerce platform and tools but could not afford the large investment. Instead, they started using machine learning to personalize customer experiences like website content and email promotions. This provided a scrappy solution that drove engagement and eventually revenue increases without requiring significant technology changes. Some lessons learned were that cross-functional collaboration takes time, testing is important for machine learning, supporting extra work from teams is key, and data science skills can be developed in non-traditional employees.

1. How and Why Shutterfly is Using
Machine Learning to Personalize
Customer Experiences
Keynote – Michele Anderson
2019 Customer Experience Leadership Forum
May 1st, 2019 Intercontinental Hotel San Francisco

2. Our Situation in 2017: We are at least 10 Years behind best practice Customer
Experience, and Marketing and Promotion Tools and Technology, with Legacy
Systems driving extremely Costly and Time Consuming Improvements to
Customer Experiences
• We were offering the same promotions to all customers
• We presented the same website experience to all customers
• We were sending the same emails to all customers (with the exception of a small set of
trigger streams)
• Minor improvements to our legacy CX either required significant engineering roadmap
investment or were not possible
• We had implemented an ecommerce platform ten years ago and then promptly customized
it so that we could not benefit from the subsequent ten years of enhancements
• We had implemented a home-grown promotion engine twelve years ago that provided very
limited functionality and flexibility
We faced multiple years of development and implementation time and a significant investment to catch up.

3. What did we want to do about catching up?
We would love to leave behind our customized legacy tools and platforms and
start afresh, but as a public listed company we did not believe that our
shareholders would stomach such a significant investment
• We faced this same question every year in the interim years and we kept
putting off making a big investment to improve how we market to customers and
promote our products ….
• … Which has only added to the size of the investment required
• …. And exacerbated the impact that the old tools and technology is having on
our business performance
Does this sound familiar to many of you?

4. What did we actually do? We committed to a subset of investments and
agreed to find scrappy solutions for the rest of our needs
• We invested in improving our marketing tools and technology, which was going to take 18 to 24 months to implement
• We were not able to fund improving our ecomm platform and promo engine
• … And so we explored options where we would not have to wait for the new marketing tools and tech and could work
around our legacy eComm and promotion tools
• Within our customer analytics team, we found data scientists who were keen to experiment with using Machine
Learning (ML) to personalize our customer experiences; it turned out that they had already started experimenting in
late 2016 under their own steam – they were testing using ML to determine what sort of custom content to present to
customers on our website
• We were able to use ML to analyse the data that we already collect from customers and their behaviour (e.g., prior
purchases, browsing behaviour, demographics etc.) we would test whether we could determine interested in product
ideas and content around specific topics such as, travel, wedding and kids
• The early test results were positive in the sense that using ML to personalize content drove additional customer
engagement, although not necessarily incremental revenue
• I concluded that we could use these early tests as a basis to go much further in personalizing customer experiences;
it did not matter that customers did not buy more from our first efforts. What mattered was that we could start testing
new experiences and not have to worry about our tests negatively hurting our business…. i.e., that we could keep
testing until we learned more about what customers were really interested in, which in the end would drive
incremental revenue
And how did we do it?

5. First, we had to pick a place to begin; we decided to start with personalized
content and offers presented on the site and in emails
 Using ML is an inherently cross functional pursuit, which meant that it took much longer to bring people
along and rally support to implement ML because we had to get support from many teams across the
organization. We had to involve the Customer Analytics team (to provide the scientists), the Marketing
team (to design and send the emails), Pricing and Promotion (to come up with a large pool of products and
offers), the ecommerce team (to enable personalized content on the website) and the creative team to
design personalizable content wells for the site.
 Because our ML efforts involved changing our work approaches (e.g., presenting variants of the home page
presented to customers) and a number of teams having to do more work (e.g., email team setting up and
testing ten versions of an email rather than one, promo team setting up and testing ten versions of a
promotion rather than one, creative team designing a new well on the home page that was set up to
present flexible content) it meant selling in the concept to the senior members of all of the cross functional
teams to get their buy in and resource commitment and this added to the significant time required up-front
 In addition, because implementing our first phase of ML work did not require product and engineering
work, this created its own issue because when teams are left out that are usually in a more powerful
position, this created more hurdles … and so it took even longer to get everyone in support of all the
changes.

6. What has happened?
 The first thing we implemented using ML at scale were personalized promotions sent via email. This
required a significant amount of additional manual work from our email team because our new marketing
investments were still being implemented; it was very important to support this team and share their
successes and communicate how meaningful their extra efforts were
 In 2018, we drove incremental customer engagement. This year we are generating both incremental
engagement and incremental revenue, which is a terrific outcome for both our customers and our
business
 Because we have been able to prove out ML success with customers, we have secured funding for ML-
driven personalized promotions across all channels, plus both ML-driven personalized content and
personalized promotions on-site
 We now have enough momentum and excitement for ML-driven efforts that we can move much faster
with our cross functional teams and get approval for investments

7. What are some of the lessons that we learned?
 Don’t give up after three meetings of trying to get everybody on the same page. It took us 8 meetings to reach
a shared consensus and commitment on our first major roll out; whereas in a non-cross functional
environment (i.e., where all participants report into one organization), you might achieve consensus and
commitment in one meeting
 Committing to lots of testing is critical with ML, because you don’t know where you will find the successes
 Using ML can turn out to be a scrappy solution because it does not necessarily require significant technology
roadmap support
 It is very important to support the team involved, share their successes and communicate how meaningful
their extra efforts are – in our case, the marketing team had to go above and beyond with manual efforts,
because we have not completed our new marketing tool implementation, and this is important to keep our
team motivated
 Although time is scarce for team members with ML skills, you will need to free up time for your lead data
scientists to become evangelists and teachers to develop these analytical skills in other team members.
 Your data scientist might not actually be primarily trained as a scientist (e.g., business analysts can attend data
boot camps for 3 months full time and gain these skills) and you might have to find them outside the Bay Area;
go where the talent is
That is enough talking from me. What questions do you have?