From Alexa and Siri to the use of chatbots as part of customer service, conversational commerce is already present for many in our everyday lives. This summary PPT examines what has enabled the recent rise, the state of play today, how might it evolve in the future and what the implications are for organisations. Conversational Commerce Today As part of work to support Mastercard’s thought leadership position on the Future of Conversational Commerce, unveiled at Money 2020 in June 2018, Future Agenda conducted desk research and a series of expert conversations to better understand the current state of play and to uncover emerging shifts of note. The future of trusted conversational commerce will be shaped by three inter-related themes. These are: 1. Enhanced Customer Experience - As conversational commerce meets proof points of viability, feasibility and desirability 2. Being Human - As bots (and humans) become more informed, more natural and more helpful, powered by real-time AI and fuelled by organisation-wide accessible flows of data. NLP improvements add other ‘human’ psychological elements, e.g. hesitance. 3. Data’s Richness - As the economic imperative to identify and make best use of data – whether generated or procured from multiple sources - remains at the heart of ecommerce activities. With pervasive AI in place, it’s the data, not the engine that differentiates.

1. The Future of Conversational Commerce
Insights from Multiple Expert Conversations
Initial Perspective | July 2018

2. Context
We would like to thank Mastercard for supporting this open foresight programme
to explore the future of conversational commerce. We welcome your
thoughts and additions to improve this initial perspective.
Mastercard Money2020 Press Release - June 2018

3. Thematic Shifts
Building on insights from Future Agenda discussions and additional dialogue in
Q2 2018, we have identified three inter-related themes around which the
development and impact of trusted conversational commerce will be shaped.
Enhanced Customer
Experience
MainstreamReadiness
Greater Relevance
Interoperability
Hyper-personalisation
Safe&secure
Being
Human
Natural Language
Visualisation
Honest Conversation
AppropriateConversation
Goodfriction
Data’s
Richness
Clear DataValue
DataLiability
Ethical Commerce
MachineLearning
DrivingAccuracy
DataOwnership
Trusted
Conversational
Commerce

4. Theme 1 - Enhanced Customer Experience
Conversational Commerce meets proof points of
viability, feasibility and desirability
MainstreamReadiness
Greater Relevance
Interoperability
Hyper-personalisation
Safe &Secure

5. MainstreamReadiness
Fuelled by consumer uptake of a plethora of channels (e.g. chatbots, voice, apps) –
and improved technological offerings such as NLP and ML, conversational
commerce in 2025 provides ‘opti-channel’ integrated experiences.

6. Greater Relevance
Developments in NL conversation, underpinned with Machine Learning enable
more relevant responses to the consumer, both within and across experience
domains: Devices become smaller, more embedded and more personal.

7. Interoperability
Channel and interface barriers dissolve with the customer, and not the brand, put
centre stage. Customer experience improves dramatically as customers are given
what they need, where they need it through the most appropriate interface.

8. Hyper-personalisation
Conversational commerce aims for a hyper-personalised experience, replicating that
in the physical world where relevance, meaning, timing (e.g. of replies) are all suited
to the context of the experience and which add up to ‘conversational momentum’.

9. Safe and Secure
The multi-layered approach of Prevent, Identify and Protect is rigorously employed
by payment facilitators. Improvements gained in fraud detection allow for more
focus on authentication and choice management across channels.

10. Theme 2 - Being Human
Bots (and humans) become more informed, more natural and more helpful, powered
by real-time AI and fuelled by organisation-wide accessible flows of data. NLP
improvements add other ‘human’ psychological elements e.g. hesitance.
Natural Language
Visualisation
Honest Conversation
AppropriateConversation
GoodFriction

11. Natural Language
A more natural human and real-time conversation is made possible through NLP, with
an AI front-end and vast, inter-connected data sets as a back-end. Conversation has
flow, cadence, inflection, timing, etc. It feels right, even though we know it’s a bot.

12. Visualisation
Using visual effects to bring to life digital commerce, conversational or transactional,
in order to enhance the experience for the user. Stimulating user imagination,
especially with VAs, also features as ‘you see her (him) better if she’s not there’.

13. Honest Conversation
As issues of trust continue to challenge brands across a wide canvas, consumers
engaging with conversational commerce demand transparency on who or what they
are speaking with as well as the agency of bots or VAs and their recommendations.

14. Appropriate Conversation
The ability to access a customers relevant data across channels enables
organisations to provide the right assistance in the most appropriate
way with the right mix of man and machine.

15. Good Friction
More complex and higher risk transactions may justify higher levels of scrutiny by
customers and brands. In the long term ‘good friction’ with conversational
commerce, wins over frictionless for most complex / higher-risk commerce decisions.

16. Theme 3 – Data’s Richness
The economic imperative to identify and make best use of data – whether generated
or procured from multiple sources - remains at the heart of ecommerce activities.
With pervasive AI in place, it’s the data, not the engine that differentiates.
Clear DataValue
DataLiability
Ethical Commerce
MachineLearningDrivingAccuracy
DataOwnership

17. Clear Data Value
Organisations seeking to transact have to be clearer about why they value
specific types of data, on what terms and how they will deploy it (esp. beyond
first disclosure) or they risk losing public trust and their license to operate.

18. Data Liability
Storing some kinds of data could come to be seen as a liability as the mere act of
storing – and with it, protecting - erodes user trust, meanwhile the costs of
securing data can be seen to outweigh the costs associated with losing it.

19. Ethical Commerce
Increased outsourcing of decisions to autonomous machines feed the debate on
the accountability and ethicacy of machines using our data - and their regulation.
The more democratised datasets become, the greater potential to drive behaviour.

20. Machine Learning Driving Accuracy
As usage of “AI” advisors for commerce (and more) increases, the data pool and
contextual learning is enriched. Machine learning feeds on this ‘training opportunity’
and in return improves significantly, rewarding brands and users with accuracy.

21. Data Ownership
With data’s value still on the rise, and customer familiarity of data exchange for better
service increasing, traditional models of ownership of digital data cause debate. The
focus shifts from ownership to the question of who is benefitting and fromwhat data.

22. Conversational Commerce – Implications, Opportunities, Key Questions

23. Impact on Customers
Implications
Increasing ease,
and convenience
for all brand
interactions
Soulless “digital”
channels
become more
human
Opportunities
Personal agents
conduct
commerce on
my behalf,
involving me
when required
Threats
Security and
privacy concerns
Concentration of
power in the
hands of my
virtual agent
Discovery may
become harder

24. Key Questions for Customers
Which brands do I trust and what do I trust them to do for me?
Who makes it easy and enjoyable for me to discover, buy and service?
Where is my data and how valuable is it / am I to brands?
What are my views on data privacy and protection?
What is my ‘social signal’ and influence footprint?

25. Impact on Brands and Retailers
Implications
Accelerating
shift from
ecommerce to
mcommerce to
conversational
commerce (and
after that) to
‘community
commerce’ Opportunities
Representation of
offering and
services within
customer’s
conversation
Shift marketing
from ecommerce
and app
conversion to
relationships
Threats
Being left out of
the conversation
by the customer
or messaging
service
Inability to collect
and apply data
across channels in
real-time

26. Key Questions for Brands & Retailers
What use cases lend themselves to conversational commerce?
How will we ensure we are part of the conversation?
How (quickly) can we build integrated, seamless and consistent experiences across access points and channels?
What data do we have / need to integrate and to what end?
How will we build deeper, more meaningful and appropriate ‘conversational’ relationships?
What new areas of the ecommerce economy should we be investing in for the future?

27. Impact on Banks
Implications
Brand visibility
removed at
point of
purchase,
removing
payment and
credit provision
opportunity
Opportunities
Real time
transaction alerts
Enhanced data,
security and
privacy
Partnerships &
Brand Alliances
Threats
Removal of card or
card use from
wallet
Disintermediation
by platform
providers

28. Key Questions for Banks
Can we locate a valuable role within the ‘conversation’?
How will our brand show up across the transaction?
How can the data we hold help the customer and retailer?
How do we mitigate against disintermediation?
Are we ready to participate in deeper collaboration with others?

29. What do you think?
As an open foresight programme we’d welcome your thoughts to help build a
stronger perspective. What do you agree with, what don’t you, what is missing
and what will be the key impacts and implications. Thank you.

30. FutureAgenda
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