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Summary; Consumer Analytics Using Big Data
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Consumer Analytics,Meeting 1. Introduction
Big data and consumer behavior. Imminent opportunities.
Introduction
Big Data are often characterized by three V’s:
- Volume: refers to the ‘bigness’ property
- Velocity: refers to the rate at which the digital processes make Big Data even
bigger
- Variety: refers to new formats and types of data
In marketing, the main driver of the interest in Big Data is the potential usefulness of it
for informing marketing decisions and executing marketing campaigns.
Big Data and consumer decision-making
The authors discuss the Big Data phenomenon and its intersection with consumer
behavior (CB). They use a narrational device that moves along the steps of the
consumer decision-making process. The steps are problem recognition > search >
alternative evaluation > purchase behavior > consumption > post-purchase evaluation
and > post-purchase engagement. According to Blackwell et al., the exchange
relationship between the customer and firm emerges from consumer problem-solving
activities.
Problem recognition
In the first stage of consumer problem-solving, the consumer sees a gap between
what he or she has (or has experienced) and what he or she wants (or wants to
experience). Companies can sense when this moment is there from a variety of
sources (e.g. search queries, social media, addressable advertising) and direct
marketing response.
Much discussion about products and issues with products takes place in social media
environments that can be monitored. This can be used to identify new products and
improvements. Moreover, companies are able to detect that product usage is slowing
or stopped by using Ip addresses (IoT = internet of things). The IoT refers to devices
that are connected via the internet. Therefore, it is possible that IoT artifacts might
upload nonverbal reactions to advertising.
The consumer may also provide early warning signs on social media that there are
problems with the relationship and companies have the opportunity to learn about the
issues from the above sources and address them.
Qualitative research methods such as in-depth interviews and focus groups have
traditionally been used to recognize problems. Such methods can be used to improve
Big Data. For example, an examination of what is being said on social media about a
brand could help prepare discussion outlines for face-to-face discussions with
consumers.
Search
,In the digital world, a problem is having too many alternatives. The enriched search
process now throws off digital data at every turn. In the past, a direct-to-consumer
retailer would have a record of all items that were purchased, but now a retailer can
record all of the search activities on its website (or app) that leads up to the purchase
as well, such as log recording all activities on the site including which items have been
searched, clicked on, abandoned, purchased, and so on. It also knows which search
items attracted prospective consumers.
Blackwell et al. emphasized the difference between routine problem-solving, limited
problem-solving and extended problem-solving. Search effort is greatest for the latter
category. It is presumed that high-involvement choice will necessarily throw off more
data than low-involvement choice and will involve different types of data. The amount
and type of data will further vary depending on the product category and its mix of
digital versus non-digital attributes.
Alternative evaluation
The e-tailer will have data on alternative evaluations including consideration sets and
inferred choice rules based on navigation sequences. Shopping cart abandonment
can signal that customers are making price comparisons at other sites. Search
behavior in general can provide additional hints as to the way the consumer is
planning to evaluate alternatives.
Purchase behavior
Data sources include cameras in stores, mobile purchase activities on branded apps,
scanners at checkout, and so on.
Consumption
Media consumption is almost fully digital at this point of time. The offline part of our
world is shrinking. The quantified self and measured life are popular names for how
we allow more of our actions to be recorded. The IoT will accelerate this trend,
creating digital data from more types of consumption. For example, Strava records
cycling/running workout activity and upload it automatically to Facebook and wearable
technologies such as watches and Fitbits record biometrics. The IoT will accelerate
this trend, creating digital data from more types of consumption including that
generated by using cars, vacuum cleaners, washing machines and so on. Devices
may reveal highly intimate psychological details about the wearer, possible including
the three classic responses pleasure-arousal-dominance. Consequently, managers
will need to find ways of using these new data sources to understand their customers,
improve the execution of their marketing programs and make their products stickier.
Post-purchase evaluation
Customers evaluate the gap between their expectations and their consumption
experience during and after consumption. Positive or negative gaps may be described
online in reviews, tweets, photos, ect.
Post-purchase engagement
Product reviews are the prototypical Big Data exemplar. These exhibit all of the three
V’s. All of which have been shown to impact the reader. It is interesting to note that
, one review writer can have an impact on later writers and that reviews are subject to
intertemporal effects. In addition to product reviews, the sources of post-purchase
engagement are particularly numerous and include mobile apps, check-in platforms,
retweets, comments made during public service exchanges and other forms of e-word-
of-mouth and so on.
Exogenous factors
Detailed information can be integrated from weather sources, private list brokers,
voting records, highway sensors monitoring and numerous other sources. Varied Big
Data can provide a more holistic view of the consumers’ journey.
Problems associated with Big Data
Big Data can provide information complementary to traditional consumer behavior
methods while also providing an advantage to marketers. However, there are various
negative aspects as well.
Big Data come from the past
Big Data is ‘Big’ about the past. However, prescriptions for the future are more
actionable. Theories and models provide such prescriptions. Big Data can be used to
generate insights that inspire theoretical explanations, test theories and calibrate
models, but have little value without a theory and/or model to provide an explanation.
Big Data record what customers did, but not why
Motivation and attitude can only be inferred from many of the Big Data sources they
describe. One solution to this problem is to supplement Big Data sources with Little
Data from more traditional research methods such as surveys. In the past, a large
amount of consumer behavior has relied on survey samples and experiments where
consumers were asked about their attitudes, intentions and behaviors. While attitudes
could be measured, the representativeness of such samples has often been
questionable because of poor response rates and sapling frames. However, Big Data
offers the possibility of having records of behavior for all current customers. The
importance of understanding constructs such as motivations, cognitions, emotions,
ect., will not diminish, but the question is whether such constructs can be inferred from
behaviors. Another way to view the change is that in the past, attitudes and other
constructs at time t were used to explain behaviors at time t + 1. In the world of Big
Data, behaviors at time t affect attitudes and other constructs at t + 1. Surveys will
continue to be used, but the advantages of using records of customer behaviors are
intriguing and create opportunities to extend consumer behavior research.
Big Data quality cannot be assumed
Maintaining a clean database requires a substantial effort and the task of preparing a
data set for analysis will often take longer than the analysis itself. There can be
conflicting data and no way to know which version is more current.
Big Data sets may not be representative
Marketers should inquire about how the data were sampled and potential biases
created by the sampling procedure. For example, a company’s data may be detailed
and numerous but only about long-term customers. In this case, there is the problem
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