Engaging consumers through
conversations: a management framework to get it right across converged channels
Paul Pangaro, Hagen Wenzek
2014-01
Paul Pangaro is CEO of General Cybernetics, Inc.,
applying conversational models to content publishing, enterprise
collaboration and education. His career spans product strategy,
prescriptive innovation and organizational dynamics, with roles as
technology executive, professor, entrepreneur and performer. He spent ten
years in Silicon Valley as CTO and strategy consultant to
startups, including a stint as Distinguished Market Strategist at Sun
Microsystems. He taught the cybernetics of design at Stanford
University, currently teaches at School of the Visual Arts in the
Interaction Design MFA program, and has lectured in São Paulo, Paris, Berlin,
Vienna, Toronto, and in cities in the US. He holds a doctorate degree in
cybernetics and an undergraduate degree in computer science/humanities.
Email: pangaro@generalcybernetics.net
Twitter: @paulpangaro
Hagen Wenzek is the principal and founder of Freestyle Consulting, an
advisory business that helps companies navigate the digital ecosystem. His
experience as the Chief Technology Officer of media holding company IPG
Mediabrands, at IBM’s Corporate Strategy, and with IBM Consulting led him to
the intersection of technology, media, marketing, and consulting services that
forms the center of gravity for widespread innovation. Start-ups in digital
marketing and big data look for his advisory on how to become a trusted
business and mature corporations leverage his consultancy to work with these
innovators. He holds a doctorate degree in electrical engineering and a number
of advisory board positions with those very start-ups.
Email: hagen@freestyle-co.com; Twitter: @hagenwww
Abstract
Marketing performance is heavily dependent on the level of
consumer engagement with a product or brand. Delivering a brand message has
traditionally been a major means to engaging consumers, but the messaging was
limited by the nature of a one-to-many communication that only allowed for very
limited feedback from the consumer. With the advent of digital convergence, the
transition from communication to conversation becomes not only economical for
the brand, but is expected by the consumer.
However, to effectively and efficiently manage a
conversation through all the traditional and new media channels with many
– if not most – of a brand’s consumers requires a rigorous approach
and comprehensive methodology. We are presenting a framework that helps a brand
or their agency to understand and actively manage all the critical components
of a conversation. Following the CLEAT-Framework – Context, Language,
Exchange, Agreement, Transaction – sets up a
conversation for success and allows a measurement of progress along each stage,
from understanding the situation of the consumer all the way to (ideally) a
commercial transaction. With or without an immediate commercial transition,
engaging consumers in true conversations is the most effective way to build
persistent relationships of trust and loyalty – the goal of high
performing marketing.
Keywords: Conversations, consumer engagement, marketing
effectiveness, marketing performance, digital convergence
Marketing Performance
The desired effect of
marketing is to drive sales of a product or service while commanding a higher
price, as justified by the consumer valuing this brand more highly than that of
a competitor. The means for doing so is to reach the consumer with the right
message at the right time. To send this message, the marketer can use a growing
number of media channels.
The choice of
channels, messages and their timing determines how effective marketing is. Determining
overall performance, however, also requires factoring in the efficiency of that
process. For the longest time the impact of improving efficiency was small
compared to the cost of using the channel, i.e., paying the price of
advertisement inventory; therefore, optimizing efficiency did not garner much
attention. This shift in importance is reflected in the development of
dedicated media agencies that manage the process of delivering a brand message
on behalf of a brand owner:
Until the late 2000’s
media buying was part of the creative activity of designing the brand message
and had rarely been managed separately. Then media buying was carved out and
consolidated across many accounts to leverage supreme negotiation power and
decrease the price for media. Only after the media buying process was separated
from the creative process could each of them be discreetly optimized., This optimization focused on effectiveness of marketing
tactics, e.g., by targeting specific audiences rather than mixing demographies
to improve outcome. Now, even further advancements are being made in the
improvement of efficiency in managing, buying, placing and interacting with
media and messages, exemplified in the acceleration of programmatic trading.
Digital Convergence
Back in 1978 Nicholas Negroponte saw the emergence of Digital
Convergence [Brand, S. 1987]
[i]
as the coming together of all compute, print and broadcast media. Later it
became clear that it is not just the mere “coming together”, but the “takeover
of all forms of media by one technology” [Mueller, M. 1999]
[ii]
— the underpinning of all content, communication and computing by digitization in general.
Before digital convergence, each medium that transported a
message could be managed separately and the end-user experience was highly
controlled around a captive consumer. Thus brand messages were tied together
only by a common brand strategy, while the execution happened independently for
both creation and transport – and therefore expensively. With a converged
marketing channel mix, that situation changed profoundly. What is built once can
now be used for multiple channels, yielding efficiency gains through reuse but
also increased effectiveness that emerges from a consistent experience for the
consumer. That is complemented by metrics that describe performance
consistently across all consumer interactions on all channels
The disruptive nature of that transformation makes it
self-reinforcing. Cost reduction is inherent in digital channels and releases
funds for technology that further drives digital convergence. Taking all these
developments into accounts led us to adapt the very definition of digital
convergence:
§
Content is not tied to a specific format anymore, but becomes independent and easily
transformed from one format into another.
§
Communication is not delivered from a central instance anymore, and control moves to the edge
of the network where the consumer drives interactions that may happen at any
time, on any device, and in any direction.
§
Computing vanished from servers owned or controlled by the brand and becomes a service
delivered from the cloud.
Therefore digital
convergence describes the decoupling of content, communications, and
computing from the constraints of physical media, devices, and machines through
digitization.
Consumer Expectations on Conversations
As digital convergence has a large impact on marketing
performance; it also raises the expectations of the
consumer towards interaction with the brand. The ultimate digitally converged
media scenario is social networking. It is not only a media channel itself, it is a multidirectional communications medium that
relies on personalized content accessed over multiple devices and leverages
vast cloud computing resources. But more than anything, it is allows a brand to
draw a consumer into a conversation.
A conversation is an
ongoing engagement in which the participants receive a benefit, where the
benefit may be in the realm of new or changed ideas, understandings, beliefs,
or intentions, which in turn affect further actions and transactions.
[Dubberly, H., and Pangaro, P. 2009]
[iii]
Since
a rigid definition is absent from today’s
technology and media world, the following sections present a breakdown of the
actionable elements as well as quantifiable metrics of a conversation. In
short, however, learning how best to foster conversations to meet and exceed
consumer expectations leads to higher engagement that, as we will show below,
leads to greater marketing effectiveness.
The vast possibilities opened by conversational engagement
come with considerable responsibilities and it is therefore mandatory to
understand what having “an efficient and effective conversation” [Blair, G.
1992]
[iv]
really means:
§
You must make your message understood.
§
You must receive/understand the intended
message sent to you.
§
You should exert some control over the flow of
the communication.
From a brand management perspective the responsibility for
having the recipient understand the message and governing the conversation lies
fully on the side of the brand. It is the expectation of the consumer that the
brand will leverage all the converged channels available to that consumer. In
the example of social networking this would mean using the same “hashtag” on
each site and channel; moving a conversation forward that might originate on TV
or print through interaction on Facebook; or, transitioning seamlessly from
Twitter to the owned media website to fulfill an aspired transaction.
Engagement
All aspiration of a brand to achieve marketing effectiveness
hinges on a high level of consumer engagement. While consumer satisfaction was
historically seen as a key indicator for a strong brand relationship, recent
research [McEwen, 2004]
[v]
shows that it is far better that “engagement […] be used as a proxy measure of
the strength of a company’s customer relationships based on the extent to which
customers have formed both emotional and rational bonds with a brand”.
That new insight led to follow-up research going deeper into
the differences between new and existing customers. Before digital convergence,
that distinction was of primary interest in direct marketing, because only
there could the message be targeted and tailored to the recipient with
sufficient precision. Now, however, brand messaging through digital and social
channels can easily take very specific information about the consumer into
account. Therefore, understanding different types of engagement is not just
relevant, but becomes critical for marketing effectiveness.
The main aspect for new consumers to evaluate a brand’s
product or service is how much they rely “on attribute-level information”
[Patterson, P. 2000]
[vi]
and “on tangible and often extrinsic cues” [McGill, A. and Iacobucci, D. 1992]
[vii]
which can be exemplified by the “generation of comparison standards”. This
would very much explain the success of digital media sites and special sections
dedicated to reviews, such as Amazon.com or Yelp.
Existing consumers often do the opposite of what is observed
for new ones and “often blend negative statements out and emphasize positive
ones” [McGill, A. and Iacobucci, D. 1992], thus act much less on the
information that others provide about the brand, but on a conversation with the
brand itself.
Measuring the difference in engagement via, e.g., a
Link-to-Conversion-Rate on Facebook shows the dramatic difference. A study in
collaboration with UNIFIED Social, a social media service provider, of 2,500
unique Facebook ads (see tables 1 and 2 below), showed that existing consumers
(proxied as ‘fans’, see Table 1) were more than seven times more likely to
follow through a conversation than new users. The incremental cost for these
conversions, i.e., one further step in a conversation, was also 75% lower (see
table 2). Therefore one can deduce that an engaged consumer can be moved along the marketing worfklow much more successfully and
at lower cost. So marketing effectiveness – the measure for how well
money is spent to achieve a marketing goal – goes up.
Table 1: Brand Strategies and Targeting Characteristics
[Friedman, D., 2013]
[viii]
Table 2: Brand Strategies and Targeting Characteristics
[ibidem]
CLEAT-Framework
As we can now safely assume that marketing effectiveness is
positively correlated with customer engagement and a conversation is a means to
increase and sustain engagement, the question is how to manage conversations
between brands and consumers.
Looking at the background of digital convergence and
marketing effectiveness makes it clear that managing conversations is a very
complex activity:
§
Engaging
a consumer is key to make marketing more effective.
§
Conversations
need to occur across all digitally converged media channels to be
comprehensive.
§
Conversations
need to be highly tailored to the customer, domain and every stakeholder’s
expectation.
Coping with that complexity is best done
by leveraging a framework that lets the expert focus on distinct elements.
In management consulting, frameworks are broadly used to identify and break
down problems as well as provide guidance towards finding a solution. Thus a
framework is not a detailed procedure to be followed, but a guidebook and needs
to be used as such.
Our framework provides insights into five distinct
components that, when instrumented effectively, will lead to successful
conversations, specifically when the goal is to increase the number of
commercial transactions through effective marketing. These components are: context, language, exchange, agreement and
transaction.
Context
Traditional mass media communication takes little contextual
information about the audience into account, because it is not conversation but
just the delivery of a message that has been the goal. However, digitization of
media enables (big) data and intelligent algorithms running on cheap, scalable
(cloud) computing infrastructure to harvest that very data and to derive
insights about the consumer’s context. That context can then be used to make
the conversation highly relevant.
Seeking to answer the following questions helps to decide what data to analyze
and how:
§
Whom to talk to? What is the right audience?
§
When to talk to? What is a good moment?
§
How to talk? What is the best channel?
§
What to talk about? What is the best content?
Audience
Selecting the right audience is a domain of extensive
research in itself. Traditionally in paid media, the audience reached via
advertisement is based on the demographic segments selected, e.g. ‘females
18-35’. In digital media, targeting can become much more refined because the
attributes that are known about individuals accessible on a digital channel are
exposed to advertisers in groups or even individually, e.g., by retargeting
based on previous digital activities (via ‘cookies’) or alignment with email
address lists from customer databases. Most sophisticated service providers can
offer audiences based on previous results of campaigns from other advertisers,
which can reveal unexpected audiences with whom to start a conversation.
Another angle for selecting an audience is to take the
viewpoint of profitability. Here, building an audience profile that correlates
with the characteristics of a predicted high Customer Lifetime Value (CLV)
would align not just with marketing goals, but overall business strategy. (For
CLV characteristics see e.g. [Reinartz, W. and Kumar, V. 2003]
[ix]
and for a system to calculate a Social Lifetime Value e.g. [Katana]
[x]
).
Moment
The right timing for sending a message to initiate or move a
conversation forward is a critical but often mismanaged aspect of context. Using detailed data about
campaign interactions between brands and consumers, combined with the intrinsic
aspects of the channel, creates actionable insights about the right timing that
were just guesswork before. Observing those interactions lets us apply more
scientific rigor to data gathering and analysis especially on social networks.
For example, by looking at the level of engagement that a promoted story on
Facebook achieved during different time windows after an event, a dramatic drop
is seen for periods longer than 24 hours [Unified 2013]
[xi]
.
This is a combined result of how Facebook ad serving algorithms place the story
as well as consumer expectation. As other channels and networks have different
characteristics, running experiments to understand them individually will be
critical.
Channel & Content
Content itself will be discussed under the ‘language’
component of the CLEAT-framework below. However, contextual information about
other content relevant to the conversation partner across multiple channels is
key to selecting the right content. Usually, the context of a consumer engaging
on a single channel is analyzed through focus group studies and extrapolated to
a larger demography. However, under the umbrella of “companion devices” or
better “companion channels”, content delivered via one channel, such as TV, is
complemented by activity over another one, such as Twitter on a mobile device. This
companion-activity offers the unique opportunity to understand the context of
an individual consumer’s engagement and thus to tailor the message much more
precisely to drive the conversation.
Services that provide correlated analysis have become available
from established media metric companies such as the Nielsen Twitter TV Ratings
[xii]
.
Leveraging Natural Language Processing (NLP) decreases the
effort to identify relevant conversations, as one is not limited to finding
specific terms, but software automatically extends the search space to terms
with the same meaning (e.g., see [Morrison, A. and Hamby, S., 2012]
[xiii]
).
Language
A shared language is an obvious requirement for any
meaningful conversation. However, as one broadly quoted statement points out:
“It is a common misperception that language use
has primarily to do with words and what they mean. It doesn't. It has primarily
to do with people and what they mean. It is essentially about the speakers'
intentions.” [Clark,
H. and Schober, M., 1991]
[xiv]
Therefore, choosing the right language for each step in a
conversation requires much more thoughtful decisions than just selecting
whether to send a message in English or in native tongue. Any decision about
how to encode and decode information is a decision about language [Wikipedia:
Language] and each of these decisions indicates the intent of the brand as well
as that of the consumer. For the brand, that intent needs to be aligned with
the expectation it has for the conversation (which is captured in the agreements and transactions it wants to achieve
as discussed below) as well as the consumer’s intention as it might be exposed
through the context (s.a.).
Language decisions are therefore manifold and span
communication systems as broad as:
§
Natural
Human Language (English, German, Chinese, etc.)
§
Technology
platform (Facebook, Twitter, brand website, etc.)
§
Content
types (video, imagery, sound, etc.)
§
Content
forms (casual, formal, ‘native’, etc.)
§
Vocabulary
(generic or ‘insider’)
§
Jargon
and abbreviations (technical, social, etc.)
§
Level
of abstraction (about ‘why’ vs. ‘how’; reasons for acting vs. possible actions;
strategic vs. tactical)
§
Human
appeal and values (rational or emotional; logical or visceral; transparent or
tacit)
§
Required
pre-cursor concepts (assuming the consumer knows they have a need vs.
articulating the need)
As it is clear from the scope and depth
of these characteristics, the component of language in conversation is complex
indeed. But not to address it effectively is to squander media budgets, digital
or not. Conversely, when a common language is found – or evolved over
time between a brand and a consumer – communication can happen
efficiently, because all parties can understand the intent of the conversation
and with less overhead over time. For example, the
simple press of the ‘Like’ button on Facebook is universal across all more than
1 billion users to indicate approval of and/or agreement with a piece of
content. The same is true for a ‘Dislike’ on Youtube that actively signals a
lack of agreement and indicates the intent to see less of these type of videos (or video ads).
The recent development of ‘native advertisement’, or ads
embedded into the content stream in a seamless way, strives to keep the
attention of the consumer on the ad by leveraging the same language that is
used to present a connected story.
Exchange
Exchanging ideas and beliefs in an evolving stream is the
core process of any conversation. However, if it is not evolutionary, i.e., if
the conversation does not move forward in a positive way, it will stall or be
terminated. Furthermore, the term “exchange” also implies that each give-and-take adds value to each party. That value is obvious when the content itself is appreciated, like
a good answer to a question (which is brilliantly executed in one of the most
effective companies in the world, Google, whose search results are precisely
these answers). However, observations about the conversations can also
create new insights into behavior, expectations, level of trust, predictions of
future reactions, etc,. Those insights might be much
more valuable in aggregate than each individual conversations – if they
can be harvested.
For the brand, the increase in Customer Lifetime Value might
be used as a quantitative metric of the value of an ongoing conversation
(assuming the implementation of CLV at the brand level includes behavioral
components). The CLV metric can be understood as the monetization of
engagement, which stands in direct relation to more granular metrics such as
type and frequency of exchange. All these metrics can be used to determine
further investment potential to lead the conversation, e.g., through paid
media.
Agreement
Each exchange leads to an agreement or disagreement on the
value of the conversation and thus each exchange is critical in determining
whether the next exchange will take place, that is, whether the conversation
will continue. A conversation is fragile in that either side may cut it off for
any reason, although it must be pointed out that conversations that appear to
terminate between consumer and brand inevitably continue between consumer and
consumer, spreading ill-will and negativity with potentially large consequences
(see e.g. [Evans, D. 2012]
[xv]
).
Complicating that fragile relationship is the fact that algorithms (e.g., on
Facebook) might prevent the consumer from ever seeing the next exchange by the
brand and might assume disagreement.
Using the most recent tools and services to manage system behavior (e.g., via
promoted stories) is therefore a crucial competence for leading conversations.
A shared understanding of the value of the conversation
increases the level of trust between the parties and therefore leads further
along the path to the ultimate purpose of the conversation. For the brand, that
purpose has to be predetermined before any conversation is initiated [Blair, G.
1992] and at the end “a clear understanding of the outcome” has to be given
[ibidem].
Transaction
That very outcome in the sense of the brand interaction is a
commercial transaction where the consumer buys a product or service. This has
the most direct impact on CLV through a monetary increase of past transactions.
However, as CLV emphases value over the lifetime of a consumer, the brand should use the opportunity of an existing conversation,
even when it concluded with the transaction, to re-engage using the very same
framework and make the same decisions about context and language. Just that
this time, the data collected about the previous conversation will inform the
following one to increase efficiency and effectiveness.
Success Metrics
‘What doesn’t get measured, doesn’t
get done’. As this truth is pervasive across companies, defining metrics that
quantify results of actions taken to engage in conversations is crucial to
manage brand effectiveness.
In the following table we summarized key metrics for each
stage of the CLEAT-framework. We have incorporate metrics that (a) look
directly at each action a consumer is expected to take, (b) can be collected to
represent the outcome of actions during each stage and (c) support decisions
for moving each stage forward by looking at the larger picture of, e.g., an
audience or group of existing customers rather than individuals.
Presenting these metrics in an ongoing basis to
‘conversation managers’ in a format that is compatible with that marketer’s
expectations is critical for the success of implementing the CLEAT framework.
Social media dashboards, CMO-desks, etc., are straightforward places to include
these conversation metrics. Those business intelligence systems are actionable
visualizations of big data that are being embedded into marketing departments
and marketing service provider workflows.
Outlook
Social Media is repeatedly being named one of the big themes
driving any business these days. Conversations provide a purpose to social
media as they directly correlate to brand engagement and consequently marketing
effectiveness. Marketers and marketing professionals at agencies cannot manage
large numbers of conversations effectively, let alone efficiently, without
supporting tools. As a management framework CLEAT provides a handle on the key
components that make those conversations successful.
With the increased use of analytics and automation to
execute campaigns across all channels also comes more and more process
performance data. That will help supplementing the CLEAT framework with more
precise KPIs, tools and techniques to further improve marketing effectiveness
at lower and lower overall cost.
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