Databases Reference
In-Depth Information
predict the behavior of customers, markets, products, services, and the competition, thereby enabling
an outcomes-based strategy precisely tailored to meet the needs of the enterprise for that market and
customer segment. Big Data analytics provides a real opportunity for enterprises to transform them-
selves into an innovative organization that can plan, predict, and grow markets and services, driving
toward higher revenue.
How does this work in reality? For example, when you engage in a web search for a product,
you can see that along with results for the product you searched, you are provided details on sales,
promotions, and coupons for the product in your geographical area and the nearest ten miles, as well
as promotions being offered by web retailers. Analyzing the data needed to create the search results
tailored to meet your individual search results, we can see that the companies who have targeted you
for a promotional offer or a discount coupon have used the outcomes of behavioral analytics from
clickstream data of thousands of other people who have searched for a similar product or service,
and combined them with promotional data targeted for your geographical area to compete for your
wallet share. Sometimes all of these activities are done by a third-party company as a service and
these third-party vendors use Big Data processing and analytics techniques to provide this kind of
service.
Analyzing this data further we can see that clickstream data by itself provides insights into the
clicks of a user on a web page, the page from which the user landed into the current page, the page
the user visited next from the current page, the amount of time the user spent between clicks, and
how many times the user engaged in a search for a category of product or service. By creating a data
model to link the web page and all these measurements, we can convert simple clicks into measurable
results along with time as an associated dimension. This data can then be integrated with promotional
data or campaign data to produce the offers, which are tailored to suit your needs at that point in time.
Further to this basic processing, predictive analytical models can be created to predict how many users
will convert from a searcher to a buyer, the average amount of time spent by these users, the number
of times this conversion happened, the geographies where these conversions happened, and how many
times in a given campaign such conversions can happen. While we have had similar processes created
and deployed in traditional solutions, we can see by combining Big Data and traditional processes
together, the effectiveness of the predictive analytics and their accuracy is greatly improved.
Another example of Big Data analytics is what you experience when you shop online today. The
most popular websites offer a personalized recommendation along with products or services that
you shop for. These recommendations have positively impacted the bottom line for all the e-retailers
that have invested in this approach. What drives a recommendation engine and how does it tailor the
results to what each individual shopper searches for? If you take a step back and think through the
data needed for this interaction to happen, the recommendation engine principle works on the data
collected from search and purchase information that is available as clickstream and market-basket
data, which is harvested as lists and integrated using metadata along with geospatial and time infor-
mation. For example, if you search for a topic on Big Data, the recommendation engine will return to
you a search result and also a recommendation of popular titles that were searched when other people
searched for a similar topic, and additionally provides to you another recommendation on a set of
topics that you can buy similar to other's purchases. This data and number-crunching exercise is a
result set of analytics from Big Data.
A consumer-friendly example of everyday analytics is the usage of smart meters to help monitor
and regulate power usage. Data from the smart meters can be read on an hourly basis and, depending
