Raise your Brand's Analytics IQ - Annotated Version

Advanced analytics like Machine Learning, or ML, are typically the purview of only large companies
with Data Science and Data Engineering teams. Einstein is all about bringing the power of data
science to everyone, at scale, and often transparently so you don’t know it’s there working to
improve a critical business process.
Whether your company has a Chief Data Officer or not, chances are it’s interested in partaking in the
AI revolution. Companies that achieve the greatest value from analytics look across their business
for improvement opportunities – and Consumer Goods companies are no exception. In this session,
see all the use cases enabled by Einstein that help you deliver more seamless and profitable
consumer experiences. Take away a plan to chart your own course to raising your company’s
analytics IQ with Salesforce!

Before we get started, I wanted to share this article as an example of what a lot of
companies struggle with today – and that’s moving past backward looking reporting to
doing much more with their data. Such as predicting outcomes and prescribing optimal
actions to take in their business.
What’s funny is that this article is from 2003 – 16 years ago! Yet many companies today
claim this same challenge. It demonstrates how little has changed – but also speaks to the
investment Salesforce has made - and is making - in lowering the barriers to more
advanced forms of analytics that I’m going to talk about today.

I find the biggest barrier companies have getting more value from their data is usually
identifying the best initial steps to take beyond their current state. You can’t scale value
until you demonstrate success.
This beautiful image featuring the Chinese proverb “A journey of a thousand miles begins
with a single step” describes the process. The challenge is not just taking that first step,
but also how you move as quickly to steps 2, 3, 4 and beyond. Analytics excellence is a
journey, not a destination.
Image source: https://www.energytherapy.biz/quotes/authors/lao-tzu/the-journey-of-a-
thousand-miles-begins-with-one-step/

CPG companies like Procter & Gamble are well on their journey, but even they sometimes
struggle to make the most of their data and analytic opportunities.
Here you see CMO Marc Pritchard describing the ideal state of brand managers working
with data science, and Information Architect Terry McFadden revealing the question asked
of the business in pursuit of difficult analytic use cases.
Is this the future of successful CPG? If so, it requires an uncommon executive commitment
to analytics as well as cross functional collaboration to make it happen. Is your
organization ready to compete on this level?

It’s not only executives who must buy in - analytics value at scale is almost everyone’s
business - even your retail partners and end consumers. So understanding how to improve
your company’s analytics maturity is of interest to many stakeholders. Including these.

Why am I talking about this subject? Ultimately this is important because of the impact it can have on
your business.
Studies like this show that Consumer and Retail businesses are among the lowest ranking when it
comes to getting value from advanced analytics – which means looking beyond historical reporting
to more predictive forms of analysis.
This alone suggests an opportunity to improve, but the really interesting information here is that
companies that lead with analytics achieve much higher revenue and margin growth than the
competition. This is why analytics is the hottest subject of debate from boardrooms to water coolers.

When I talk about AI, I’m speaking about advanced analytic methods like Machine Learning - which
is distinct from traditional Business Intelligence and historical reporting. Statements like this from
McKinsey demonstrate the importance to think about AI holistically in pursuit of a portfolio of high
value use cases. Companies that do best also think about how to apply techniques across their
company - not confining efforts to a silo.

The question for many of you then is: where does our company fall on this spectrum in terms of
analytical maturity, and also how can Salesforce help us move up the curve faster, given that we
have a variety of data and analytic technologies already – like other applications, a Data Lake, a
Data Warehouse, a CDP, a DMP, Master Data Management System, various BI tools, data
integration and preparation tools, and more?
This presentation is intended to help you answer these questions, make progress and get more
value from your data with Salesforce.

To get started identifying use cases that matter most to your business, you can see here
Gartner advises a focus on evaluating ways that key customer business processes can be
improved. Sounds like CRM, doesn’t it?
You then prioritize and execute AI use case opportunities based on business value and
your capacity to execute. Test and learn, deploy the winners, retire or revise the losers.

Last year at Connections I used this diagram to explain the various packaged and custom analytic
methods available from Salesforce to inform our clouds, relative to how they support your
consumer’s journey.
The idea here was to help you identify and prioritize use cases that made sense for your business
based on your skill/comfort level, then work with Salesforce to see how we can help. With many
methods available, it all depends on your ability to execute and use case prioritization.

This year I go down a level deeper to help you advance more quickly by describing many
of the use cases supported by Salesforce. It’s not always apparent.
Analytics is all about business improvement - either creating growth or efficiencies.
Relative to your consumer’s journey and all the business moments contained within, here
are some of your goals expressed as metrics. This is an “inside out” view where we look to
map use cases to critical customer facing business moments that your consumer
experiences every day.
At this level, organizations need to understand the extent to which data and analytics
support these business processes. Only then can you understand areas to target for
improvement, potentially with AI approaches, and see how Salesforce can contribute to
your success.

First, there are a number of Out of the Box analytics capabilities embedded in various
Salesforce Clouds that can improve your results across the consumer journey - in some
cases almost transparently to your business users. Think about these use case in the
crawl phase of a crawl, walk, run roadmap where you seek to rapidly move up the
analytics maturity curve.
Some of these use cases impact multiple functional areas even if the owner resides, for
example, in marketing. The utilization of social analytics is the topmost example here,
where those insights can improve execution at many points of the journey even if the
application is used mainly for marketing purposes.
Social data and insights created can be extracted and used by many other areas of the
business - for example, in improving advertising audience performance or demand
forecasting. These are the fastest ways Salesforce can add intelligence to your business
processes.

We don’t have time to dig into each of these, but you can examine them offline using this
chart for more information about each Salesforce Out of the Box capability. Also in this
table I have identified the Salesforce Cloud where the capability resides.

Next, there are custom methods which can be developed using Salesforce and other data
using Analytics Studio and Einstein Discovery. Think about these use cases in the walk
phase of your roadmap.
These capabilities allow both administrators and analysts to leverage “citizen data science”
to develop and deploy new scores and measures into Salesforce Clouds or other systems
to support a variety of new use cases.
I would note here that companies with Data Science teams should find this interesting.
Enabling others in the organization to attack problems like these unaided frees up time to
focus on working on other analytical problems while also increasing awareness internally
of business value opportunities with analytics. Segmenting customers by liftetime value
here is the topmost example - an analytic that has applications across your business to
prioritize and focus efforts on nurturing and growing your most valued customer
relationships.

Again, here is a table with more information about these use cases you can examine later
offline. I would note that the potential use cases are numerous but ultimately depend on
your company's strategy, analytics maturity and the prioritization of use cases relative to
your ability to execute them.

Finally, there are of course any number of fully custom analytic use cases which can be
developed and deployed into Salesforce environments – given the right data, analytical
skills and integration.
Here are a few that tend to be top of mind for many Consumer Goods companies. Also
here you can see Einstein Analytics, Mulesoft, Datorama and Heroku called out as
enablers – these are key Salesforce technologies that can help even very sophisticated
companies move more quickly. Some are analysis tools, some organize and present data,
some help connect the dots. All can can be analytics fuel within your information
architecture.
Typically, data science either from internal teams or third parties contribute to the success
of these solutions given their complexity and integration requirements.

Here again is a bit more information about those use cases. These are custom and would
require data and systems integration work in addition to analytic modeling potentially with
open source tools. Use cases like these are typically for more advanced companies, but
Salesforce accelerates your ability to consider them.

While you can obtain significant value from the use cases I highlighted on the previous
slides, it’s when these combine relative to overall CX that the most value is achieved. One
example is showing support for a b-to-b-to-c scenario where brand and digital marketing,
retail partner sales and supply chain collaborate to deliver a seamless customer
experience.
As we all know, B-to-B is the traditional CPG model, but progressive CPGs are adopting
direct consumer connections via digital channel marketing, creating significant amounts of
PII, while also exploring direct to consumer sales that complement retail channels.
Combining these better aligns your brands with consumers’ purchase lifecycle and
supports more equal collaboration with retail partners.
Here are you can see how using Engagement and Social insights could be used to
improve Advertising Audience targeting to focus on existing and potential customers that
represent the most value to your brands. This is digital marketing, supporting your media
spend with greater insights into your target consumers.

Next, aligning digital media for brand marketing with trade promotion helps shopper
marketers collaborate with retail partners on targeting the highest value potential
consumers down to the region or geographic store level.
Think about in-store digital promotions, or demand-creating direct to consumer geographic
digital marketing that drives consumers to retail partner stores. Pair this activity with
improved promotional insights to align trading partner business plans with brand digital
marketing.

Further bringing supply into the process ensures promotions and brand marketing reflect
real time product availability at the shelf – ensuring both lower incidents of product
unavailability and higher consumer satisfaction, as well as improved retailer relationships.
Out of stocks has long been a problem for retailers and consumer goods companies - so
applying analytics to this problem inline with brand marketing and sales promotion is worth
exploring.

The resulting enhanced PII and deeper consumer relationships can be used to create
richer insights that power value based brand marketing, shopper marketing and trade
promotion to improve margins and retail partner relationships – for all stakeholders.
Retailers like Walmart and Target are upping their digital games by leveraging their online
audiences to sell media to brands – just another example of retailers creating more
opportunities for brands to target their consumers, but also helping draw more concessions
from suppliers in the form of discounts and trade spend.
The more Consumer Insight a CPG company possesses that a retailer lacks, the greater
the likelihood that they can spend less on trade promotion and collaborate on more equal
and productive terms.

As a guide to the improvement to expect for use cases in your business, here is some McKinsey
research showing sales and margin impacts, plus cost reduction percentages associated with
companies using advanced analytics to attack many of the use cases I talked about today.
Data points like this can help you create the business case to invest in new supporting new
methods, but at the least justify testing uses case in pursuit of improvement.
Source: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/achieving-
business-impact-with-data

As a takeaway, I suggest you document the analytic use cases in place at your company
today which support the consumer journey, and how they are delivered.
Collaborate with those in the business to understand the performance of the use cases – is
there room for improvement?
Also, what use cases have been talked about but not pursued?
You can then speak with Salesforce to see how we can help you move forward with what
you have today, document and scale analytic success, and ultimately improve your
business results while charting a path to more use cases.

Raise your Brand's Analytics IQ - Annotated Version

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