This document discusses the use of AI and machine learning to enhance customer experiences, anticipate needs, and ultimately improve ROI through personalized interactions and contextual marketing. It highlights the importance of real-time analytics, tailored messaging, and conversational marketing in meeting evolving consumer expectations, particularly in mobile environments. The document also emphasizes that understanding consumer intent and leveraging contextual data can lead to more effective marketing strategies and increased customer engagement.

1. How to use AI to anticipate, advise and improve experiences
and ultimately increase ROI
Emotional AI for meaningful
conversations with customers

2. In this paper, we’ll discuss the risk of missing the opportunity to use digital technologies
to learn about consumers’ preferences and habits. We’ll show how leveraging contextual
data and real-time analytics using artificial intelligence (AI) and Machine Learning (ML)
can reduce marketing cost, attract hyper-targeted customers and create engaging
consumer experience.
Brands like Netflix and Amazon have pushed the envelope for customer
recommendations and anticipatory service so much so that we are now entering the
‘advice era’, in which customer service is increasingly expected to be a function that
offers more than just solutions to problems[i]
.
How can your brand ensure it’s able to match the proactive, predictive service of the
likes of Netflix and Amazon and how can you harness AI technologies to improve your
customer experience today, not in the future?
On-demand and in real-time service expectations surpassing today’s smart agents who
are not yet able to learn users’ everyday life created underwhelmed customers. Yet, AI’s
ability to process massive amount of data fast and learn, has the potential, if contextual
data is used, to provide companies with insights about how they can meet their
customers’ just-in-time and just-in-place needs.
Today, 81% of buyers who encounter gated content go elsewhere and research shows
that responding to a new lead within five minutes of when they first reach out is crucial.
Respond any later than that, and there’s a 10X decrease in the odds of getting in touch
with that lead. To ensure leads can always get a response within that five-minute
window, companies have been turning to conversational marketing[ii]
.
However, interactions with consumers need to be tailored to their context.
Understanding consumer intent enhances the effectiveness of the conversation. Brands
should then tap into context – time of day, day of week, user location and weather
conditions – to tailor interaction, product recommendations, and offers, ….etc.
© 2018 LIFEdata. All Rights Reserved. lifedata.ai 1

3. A 2016 survey of 6,000 consumers, spread across North America, Europe, and Asia,
found that 9 out of 10 wanted to be able to use real-time messaging to have
conversations with businesses. That same survey found that 66% of people preferred
messaging over any other communication channel[ii]
.
For brand/consumer interaction, the user experience on mobile today primarily includes
search and browse, both of which have their own place in the customer journey. But
messaging as an interaction layer cannot be ignored any longer. Messaging is an organic
interface for a customer to ask for ‘anything’ on-demand in a private, personal
environment that truly replicates a concierge model, which was the original promise of
mobile apps[iii]
.
Mobile is a critical part of the customer journey, but most consumer brands have not
implemented successful mobile programs to date, with shockingly low ROI on all efforts.
This is because they are relying strictly on branded apps and implementing advertising
that does not take into account the uniqueness of the channel.
Messaging As The New Browser
Get closer than ever to your customers. So close that you tell them
what they need well before they realize it themselves
Steve Jobs
© 2018 LIFEdata. All Rights Reserved. lifedata.ai 2

4. The major change occurring in online chat, is that it has traditionally relied on humans
but now chatbots have arrived. According to a PwC report in 2017, 64% of consumers
said they would rather have instant access to quality customer service than preserve the
jobs of customer service reps[iv]
. However, in the same year, stats revealed that chatbots
on Facebook Messenger failed to answer queries 70% of the time[v]
. The result has been
a massive scaling back in brands using Messenger as a platform for chatbots.
When a bot functions as a customer service rep, personal shopper, or research partner,
natural language processing (NLP) proves critical but training a bot takes time and
practice.
Bots are just one component of digital technologies that could use permission marketing
strategies based on supplying targeted lifestyle content in order to foster personalized
recommendations and collect and monetize user data via cross-sell, up-sell, hyper-
targeted, and proximity marketing. Bots feed on data, the more databases are linked to
the bot, the more valuable it becomes for the users[vi]
. Up until now, bots show notable
deficiencies and often receive requests they cannot fulfil.
Be There. Be Useful. Be Quick.
IT ALL BECOMES ABOUT “ME”.
People are taking search personally. Just as “near me” is a contextual signal that people
want to find something based on their location, these searches for “me” and “I” are
signals that people expect personally relevant content. Marketers who understand
search intent and look for patterns in how people qualify their needs have a big
opportunity [vii]
.
Customers expect a personalized shopping experience. That’s one of the best ways
to increase engagement and sales. People should be encouraged to create a customer
profile on websites or mobile applications to monitor their habits and give them special
offers based on their browsing pattern or previous purchases[vii]
.
Personalization tactics make it easier to upsell and cross-sell to customers. Ultimately,
this means selling more money without spending much. It is actually cheaper to target
current customers than it is to acquire new ones.
© 2018 LIFEdata. All Rights Reserved. lifedata.ai 3
+10% Conversion rate
+14% CTR
+26% Open rate
Personalized messages to subscribers
Personalized subject lines of emails

5. LIFEdata is a human-like Conversational AI As-a-Service technology to implement
AI fast and generate value for businesses leveraging contextual, personalized
interactions.
LIFEdata learns user’s habits and preferences as they happen and delivers offers or
experiences when they are needed using real-time knowledge.
It reacts to what users do throughout the day to increase engagement based on a
combination of individual’s biological, behavioral and psychological data, to enable
individualized interaction and real time-engagement. LIFEdata personal assistant detects
the change in the user’s context and make recommendations for the new context.
LIFEData.AI’s personalized, automated conversation experiences deliver the right
conversation at the right time based on the user’s profile, enabling businesses to
manage resources and scale.
Using engagement as a service allows companies to know their marketing spend per
user and calculate its ROI, while achieving a more engaging and personalized customer
interaction.
Intelligence from Customer
Interactions
The full potential of connected devices is only achieved
when they are tied to individual identities
Gartner Report, The Identity of Things for IoT
© 2018 LIFEdata. All Rights Reserved. lifedata.ai 4

6. Each interaction with a user has to fit its right context. LIFEdata platform understands
the explicit intent of the user and the context of his life – the time of day, day of week,
his location and weather conditions – to further tailor product recommendations, offers,
etc.
Hyper-targeted Marketing
© 2018 LIFEdata. All Rights Reserved. lifedata.ai 5
The technology enables your brand to supply targeted lifestyle content in a timely
manner, cross-sell, up-sell with hyper-targeted, proximity marketing through advanced
CRM on live user data, hyperlocal advertising to support online to offline (O2O)
consumer activation. A use case would be to target shoppers who are close to a store
and offer hyper-personalized offers based on their custom profile and behavior to
increase foot traffic and sales, or understand when shoppers are at the store to make
tailored discount offers based on shopping history and consumption patterns.

7. ONLINE OR OFFLINE PERSONALIZED EXPERIENCES ARE KEY FOR SUSTAINED GROWTH
AND REVENUES.
Adidas recently unveiled its new and improved app, which is intended to be the best one
yet. The app was designed so all of the brand’s fans go through an extremely customized
experience. The app’s features include a personalized newsfeed, live chat for any
inquiries and easy access to the full glory of Adidas’ online store. The newsfeed will
include new product announcements and events, prioritized based on what the app
already knows about the user. The app factors in a user’s gender, birthday and previous
purchases to tailor this experience.
Adidas wants to ensure all their consumers, from athletes to street style seekers, have
an easy and personalized experience on the app. As more people turn to their mobile
phones to make purchases and stay connected, working to perfect an app for the user is
one of the best strategies in increasing online sales. Adidas says 60% of its online
shopping happens on smartphones. Its goal is to keep improving the app so users keep
coming back.
Online and Offline Leadership
© 2018 LIFEdata. All Rights Reserved. lifedata.ai 6
76% of websites now contain hidden Google trackers[ix]
24% percent have hidden Facebook trackers[ix]
These two companies have amassed huge data profiles on each
person, which can include your interests, purchases, search,
browsing and location history, and much more.
Princeton Web Transparency & Accountability Project

8. Companies that began experimenting a few years ago with software programs to
automate mundane tasks are reaping tangible benefits now. Hundreds of software
robots work alongside human employees at companies such as Ernst & Young and
Walmart Inc. where they’re saving employees millions of hours of time from repetitive
tasks that employees tend to enjoy less, and freeing them up to do more meaningful,
thought-intensive more focused human work.
The market for software robots, including those that incorporate artificial intelligence, is
expected to grow to $2.9 billion by 2021, up from $250 million in 2016, according to
Forrester Research Inc.
Since January 2017, EY has deployed about 700 software bots internally throughout
departments such as human resources, travel and accounting. They expect to save 2.1
million hours of employees’ time spent on repetitive tasks during the company’s last
fiscal year, ending June 2018[x]
.
At AT&T Inc., more than 1,000 software bots have taken over routine, repetitive tasks for
human employees, up from about 200 in mid-2016. A software robot that’s capable of
scanning phone calls to AT&T’s customer service division and compiling network traffic
reports has been particularly useful over the past year, employees say[xi]
.
New Organizational Efficiency
“My time isn’t spent compiling and conditioning data anymore,
it’s spent analyzing it,” said an engineer for AT&T.
Wall Street Journal
© 2018 LIFEdata. All Rights Reserved. lifedata.ai 7

9. 8
CHALLENGE
A prime food company leader in its industry is main sponsor in several sports. The
company is managing separately sponsorship, social media, website, mobile apps and
their presence on the websites of athletes and federations that they sponsor. CRM was
not smooth as they are not having a sole data foundation for the same user.
BUSINESS NEEDS
The client decided to integrate LIFEdata technology on their website, social media
channels and mobile apps to recall their sponsorships and create an active engagement
through biomarketing. Instead of managing passively important sponsorship
investments, the client went the extra-mile building a contextual initiative around
sponsorship, nutrition, their values and their offer. For example, based on the user’s
sport preference each user is getting specific educational content, personalized food
recommendations and the client’s products are proposed contextually based on the user
preferences/intent.
SOLUTION
If the user is geolocated on the mountains or liking winter sports, he will get
personalized nutrition recommendations targeted to his preferences, these sports and
location with sponsored content by winter sports athletes endorsing the company in
contextualized, personalized recommendations.
Mobile Engagement Through
Biomarketing For Premiere Food
Company
© 2018 LIFEdata. All Rights Reserved. lifedata.ai
BUSINESS CASE

10. BUSINESS CASE
9
CHALLENGE
Suisse Life Science introduced LIFEdata solutions to deliver value with actionable,
personalized nutritional, metabolic and lifestyle guidance from genetic testing.
BUSINESS NEEDS
With the increased use of the Internet for medical information, consumers have become
medical consumers not just patients. This has created a change in the doctor/patient
relationship as individuals become more knowledgeable about their own health and
want more control over their personal information and treatment decisions. Physicians,
meanwhile, are concerned about giving patients too much access to information they
may not properly understand. Even many doctors aren't well-trained in the clinical
implications of genetics and genomics.
SOLUTION
A personalized health program that fits patients’ lifestyle and habits starting from a
conversational AI genetic counsellors guides individuals towards personalized healthy
eating and nutrient deficiencies prevention. This solution enhances the citizens’
perception of public service and delivers back unvaluable insights to the institutions.
Personalized Nutrition
From DNA
© 2018 LIFEdata. All Rights Reserved. lifedata.ai

11. © 2018 LIFEdata. All Rights Reserved. lifedata.ai 10
References
[i] “How to use AI to anticipate, advise and improve experiences” By 24/7 on April 2018 https://www.mycustomer.com/resources/
webinar-ondemand-how-to-use-ai-to-anticipate-advise-and-improve-experiences
[ii] “State of Conversational Marketing 2017 ” Drift + Clearbit 2018 https://blog.drift.com/wp-content/uploads/2017/10/State-of-
Conversational-Marketing.pdf
[iii] “MESSAGE TO MARKETERS: MOBILE CHAT IS THE NEXT KILLER APP ” By Puneet Mehta, Adage, April 2015 http://adage.com/article/
digitalnext/message-marketers-mobile-chat-killer-app/297951/
[iv] “Bot.Me: A revolutionary partnership How AI is pushing man and machine closer together ” PWC, 2017 https://www.pwc.com/us/
en/industry/entertainment-media/publications/consumer-intelligence-series/assets/pwc-botme-booklet.pdf
[v] “Facebook Inc’s Chatbots Hit a 70% Failure Rate” Leo Sun, Feb 2017 https://www.fool.com/investing/2017/02/28/facebook-incs-
chatbots-hit-a-70-failure-rate.aspx
[vi] “How to Train Your Bot: Best Practices in Managing and Measuring Bots ” Robert LoCascio, Sep 2018 . https://
www.liveperson.com/connected-customer/posts/how-train-your-bot-best-practices-managing-and-measuring-bots
[vii] “The rise of personal searches: How can content marketers take advantage?” Emma Derbyshire, Feb 2018 https://
searchenginewatch.com/2018/02/16/the-rise-of-personal-searches-how-can-content-marketers-take-advantage/
[viii] “15 Email Personalization Stats That Might Surprise You ” KIM COURVOISIER - AUG 17, 2017https://www.campaignmonitor.com/
blog/email-marketing/2017/08/15-email-personalization-stats-might-surprise-you/
[ix] “Google and Facebook are watching our every move online. It’s time to make them stop” CNBC, Feb 2018. https://www.cnbc.com/
2018/01/31/google-facebook-data-privacy-concerns-out-of-control-commentary.html
[x] “No Coffee Breaks Needed: Companies Add Software Robots to Workforce” WSJ, By Sara Castellanos Mar 2018 . https://
blogs.wsj.com/cio/2018/03/22/no-coffee-breaks-needed-companies-add-software-robots-to-workforce/
[xi] “AT&T’s 1,000 Software Robots Are Doing Boring, Repetitive Work For Humans” WSJ By Sara Castellanos, Feb 2018. https://
blogs.wsj.com/cio/2018/02/05/atts-1000-software-robots-are-doing-boring-repetitive-work-for-humans/
[xii] https://www.platejoy.com/
https://lifesum.com/premium/
http://vitafive.com/

12. We believe organizations are underutilizing data. Only 13% are leveraging their
investments to realize both cost saving efficiencies and new business growth.
The right technology combinations will vary across industries, and will change over time
as technologies evolve. What’s more, the mix required to lower costs differs from that
best suited to driving top-line growth.
LIFEdata creates end-to-end Artificial Intelligence solutions for enterprise brands
who want an easier way to communicate the right information, at the right place,
in real-time to their customers.
CONNECTING THE DOTS BETWEEN LIVING DATA.
It’s not about the data, it’s about what you do with the data in terms of making sense of
it. We help clients discover hidden patterns in data and capitalize on these insights.
We transform businesses by shaping the way people interact with them.
MAKING YOUR BUSINESS PERSONAL. AT SCALE.
The most growth lies deeper in the customer experience.
We specialize in experimentation across all the customer journey to grow your revenues.
We develop tools to collect and organize data, then create interventions and platforms
that utilize the insights gained from that data for targeted interactions based on
biomarketing and quantified biology (behavioral engagement from biometric data and
connected devices).
© 2018 LIFEdata. All Rights Reserved.
info@lifedata.ai
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