The impetus behind real-time business intelligence
Updated: 2 days ago
With the marketplace and product lifecycle operating under a constant
threat of rapid innovation and change, customer intelligence holds the
key to establishing transient competitive advantage. By enabling
efficient and speedy marketing decisions, customer intelligence provides
insights for products and marketing channels to focus on. It also
provides faster, data-driven information on the satisfaction levels of
different sales and operating channels, as well as financial and
strategic information on various geographies.
The emergence of big data and consumers’ willful generation of
unstructured data have led to the evolution of organizations from profit
centers to data centers, transforming decision-making from an
intuition-driven process to a data-driven approach.
Understanding the Role of Customer Intelligence
Customer intelligence is essentially a combination of customers’
demographic data (pertaining to the individual’s
age/gender/location/income, etc.) and social and behavioral intelligence
(pertaining to customer interactions and social behavior). It has
become a critical ingredient in making effective strategic decisions,
and it’s the foundation of building a comprehensive business
intelligence system.
Most businesses tend to view social intelligence as a virtual and
unruly dimension of customer intelligence because they cannot control
the amount of unstructured data being generated. However, when combining
both the traditional and virtual dimensions, insights can be generated
into customer preferences and evolving trends.
Business intelligence encompasses all spheres of customer interaction
and has evolved in recent years from basic dashboards and scorecards to
a holistic view of customers including customer preferences and
real-time buying patterns. Customer intelligence, procured through a
vast network of customer “data-marts,” has widespread applications from
demand creation and customer acquisition to understanding why products
were not purchased.
Managing the Customer Experience
Customer intelligence has evolved from its traditional dimension of
only including demographic information to a vital tool in managing the
customer experience. It provides insights into a consumer’s shopping
behavior, product preferences and probability of purchase. Marketing
strategies such as product bundling and cross-sell/up-sell – which are
heavily dependent on consumer preference and probability of purchase –
are enabled on a real-time basis today, thanks to the evolution of
customer intelligence.
To augment traditional customer intelligence efforts, organizations now need to introduce social intelligence to make strategic decisions and obtain holistic insights. The quality of outputs and insights generated from customer interaction can be improved if social intelligence is inclusive of customer intelligence, as online reviews and customer feedback have an added element of genuineness and willingness. Organizations can design regular updates and newsletters to create awareness and interest in their dynamic customer base with the use of social intelligence.
Delivering Real-Time Results
Data privacy concerns have long been a major obstacle in utilizing
customer intelligence for effective marketing and promotional
activities. However, the advent of machine learning and abilities to
predict parts of personally identifiable information (gender, age, etc.)
through a customer’s social behavior (websites browsed, apps used) can
help organizations map campaigns and messaging to respective customer
preferences and therefore improve return on marketing investments and
customer response rates.
For example, a major retailer in the Middle East wanted to increase
revenue and profitability using personalized and targeted campaigns
during the festive period of Ramadan. While using the business
intelligence aspect of the data, the team was able to identify the best
product affiliations to devise strategies around product bundling,
up-selling, cross-selling and assortment planning. However, when
customer intelligence (demographic data) and business-level data
(transaction data) were used in combination, the team could build
real-time campaigns throughout the promotional season by identifying the
right marketing channels, depth of discount and assortment planning for
each store segment, which helped the retailer achieve double their
initial season targets.
Replacing Information Silos with Datamarts
While the case just described demonstrates the benefits of customer
intelligence, in hindsight it is a classic example of benefits that can
be drawn from a customer datamart. A well-planned datamart helps
organizations integrate data from disparate source applications and
operating systems for analysis, thereby giving companies a holistic
picture of the business. Building a datamart appears simple, at least in
terms of definition and using pre-defined steps, but it can be
complicated, tiresome and irrelevant (to the business purpose) if
important data and tool considerations are not taken into account.
While cost, ease of use and familiarity of the tool are important organizational factors, other factors such as functionality, end reports and dashboards, as well as the amount of data that has to be handled, are important considerations when selecting the tools for building the datamart. Keep in mind the following:
Data points and definitions should be normalized to at least the third normalized factor to avoid redundancy and duplicate values in the datamart.
Schema definitions should take into account the granularity of data so they are adequately defined. Only transformed and cleaned data should be included in the datamart.
Most important, when separate datamarts are established, they should not be antagonistic in nature, i.e., affect the performance of the other datamarts already established in the organization and other departments.
Enhancing Functional Benefits of the Datamart
From a functional perspective, datamarts are built with the primary aim of building statistical models to aid business decisions. However, important data considerations need to be assessed to achieve the expected and most optimum outcome from this exercise. The three important data considerations are:
Data horizon: how often or quickly the new data becomes part of the training model.
Data relevance: provides answers on whether the right assumptions have been made regarding the relevance of data in the model.
Data obsolescence: the time period before the data becomes irrelevant to the model.
The age of data silos are disappearing, and it would be wise if
organizations move from traditional modes of data collection to
integrating all sources to a centralized repository to generate the best
insights from all possible sources. There are incremental insights to
be generated from unstructured data sources, which come to an
organization free of cost.
Swaroop Johnson is a consultant for analytical solutions at Blueocean Market Intelligence, a global analytics and insights provider. In his consultant role, Johnson proposes optimized analytical solutions across retail, banking, insurance, life sciences, utilities, entertainment and technology industries. For more information, visit www.blueoceanmi.com.
