Gib Bassett

1. Gib Bassett, Customer Success Director
gbassett@salesforce.com
Charting a course to intelligent
customer experiences with AI

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3. Gib Bassett, Salesforce Customer Success
1, Consumer Place
“The home for consumer data”

4. What you should take away from this talk
1. Go back and answer the questions: “How well do we inform,
execute and measure the complete customer experience?”
2. Identify aspects of customer experience that should be improved
3. Prioritize these improvements and scope how to address them
4. Focus first on high value quick wins
5. Build and evolve a portfolio of improvements
6. Focus on measuring and communicating business value

5. 5

6. 6
Data & Advanced Analytics: High
Stakes, High Rewards

7. We focus on AI, starting with our data and
making certain that data engineering and
governance exist. We then look at ML on top
of that to solve business problems. Today
every company needs to be a data, analytics,
and algorithmic company.
CIO Javier Polit Nov 2019
AI investments are an important element of
Starbucks' digital strategy as we work to grow
digital customer relationships. Deep Brew will
increasingly power our personalization engine,
optimize store labor allocations, and drive
inventory management in our stores.
CEO Kevin Johnson Oct 2019

9. Highlights from the
Retail and Consumer Goods
Analytics Study 2018

10. Overcoming Barriers
When asked about top barriers to AI,
enterprises cited finding use cases and
defining strategy, security/privacy, risks
and integration complexity. Nearly two
of three organizations cited finding a
starting point as a concern.
Identifying Use Cases
Look at how you are using technology
today during critical interactions with
customers — business moments — and
consider how the value of those
moments could be increased.
The CIOs Guide to Artificial
Intelligence, February 4, 2019

11. Analytics in the Service of Better Customer Experience
Pre-Shop Shop Post-Shop
The Consumer
Journey
Analytic
Options
Data Sources External Data Internal Data
Use Case Complexity + Time to ValueProjects Moonshots
Applications Tools Programming
Business
Goals
Stimulate Demand Fulfill Demand
Retain and Grow
Relationships
Improve Content Relevance,
Marketing & Advertising ROI, Brand
Value, Engagement
Improve Sales, Margin, Category
Growth, Market Share, Channel and
Promotional Performance
Maximize Customer Satisfaction,
Loyalty, Referral
Metrics

12. Understand which use cases and domains have the potential to
drive the most value, as well as which AI and other analytical
techniques are needed to capture that value.
This portfolio ought to be informed not only by where the
theoretical value can be captured, but by the question of how the
techniques can be deployed at scale across the enterprise.
Notes from the AI frontier
McKinsey Global Institute

13. If you just have a bunch of little
disconnected projects, nobody’s
going to get excited. However, if you
know that each one is improving
things incrementally, when you
combine them, they add up to a
huge amount of transformation. So
a collection of low-hanging fruit is an
excellent idea.

14. Your
Consumer’s
Journey Consideration
Pre-Shop Shop Post-Shop
Browse / Buy Service / Referral
Your
Goals
Stimulate Demand Fulfill Demand
Retain and Grow
Relationships
Improve Content Relevance,
Marketing & Advertising ROI, Brand
Value
Improve Sales, Margin, Category
Growth, Market Share, Channel and
Promotional Performance
Maximize Customer Satisfaction,
Loyalty, Referral
A Cyclical, Non-Linear Journey
Metrics
1. How do analytics support this process in your company?
2. What use cases are in place? Could you improve?
How to think about your analytics opportunities

15. Source: McKinsey Advanced Analytics

16. …Rank the options …and scope the top ones

17. AI is really about experimentation and continuously
improving what you do, versus the reality you need to
master. I know it’s overused, but this idea of making it
very “okay” to encounter failure along the way is very
important in AI, not just in general innovation.
CIO Filippo Catalano

18. Coca-Cola allowed one of its coolers,
stocked with product, to serve as Einstein's
foil. Einstein Vision recognition easily
accounted for the varieties of Coca-Cola
stock within the forefront of the cooler
display cabinet.
Jack Wills is leveraging Einstein for
Commerce not only for product
recommendations, but also for site
search, and in-depth reporting.
U.S. Bank uses Einstein Predictive
Scoring to identify prospective Wealth
Clients, recommend ways to grow
existing client relationships, and pre-empt
customers from leaving.
Kohl’s uses Einstein Recommendations for email to observe behavior and
recommend products at an individual level. This allows for custom
merchandising at scale and increased exposure to Kohl’s full product catalog.