INF30030 - Business Analytics in Marketing Industry Report 2018

This report examines the application of business analytics within the marketing industry, tracing the industry's evolution from the production era to the modern data-driven age. It highlights the drivers for adopting business intelligence, such as managing uncertainty, enhancing customer loyalty, and improving decision-making speed. The report details the perceived benefits of business analytics, including predicting consumer choices, segmenting customer bases, and optimizing marketing mix. It also outlines business analytics strategies employed in the industry, including descriptive, predictive, and prescriptive analytics. Furthermore, the report addresses the challenges to business analytics strategies, like filtering relevant data and the shortage of skilled professionals. It concludes by discussing the actual benefits achieved through the implementation of business analytics, such as improved sales planning, better decision-making, and cost-effectiveness, particularly in conjunction with various marketing strategies.

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BUSINESS ANALYTICS IN THE MARKETING INDUSTRY
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Overview of the Marketing Industry
Marketing is the activity of creating, communicating, delivering, and exchanging
valuable offerings to consumers. The marketing industry has witnessed tremendous development
since the early 1900s when it began (Emblemsvag, 2015). The industry is today one of the most
active industries because companies have to seek for means of reaching the customer for them to
sell their products. Historians categorize the development of the industry in to four eras namely;
production, sales, marketing department, and company era. The production era was the period
until mid-1800s where people produced the goods they needed although they could do barter
trade to obtain the goods they could not produce (Kumar, 2015). Production was enhanced
during the industrial revolution where factories produced huge amounts of products and offered
them to customers at lower prices. The sales era started during the great depression that occurred
between 1929 and 1940s. It weakened the economy forcing producing companies to reduce
supply and resolve to other selling strategies. Sales teams became a characteristic feature of the
industry. Businesses began implementing door-to-door sales strategy and also advertised their
products on their stores and billboards.
The marketing department and company era was the period after the 2nd world war where
the economy had recovered from the perils of the great depression. The customer-oriented
business strategy began as firms started producing products as per the needs of customers. Firms
considered themselves more as marketers than producers. Also, companies created marketing
departments after which some investors came up with marketing companies. With the rise in
marketing, telemarketing gained prominence between 1960 and 1970 (Evans & Lindner, 2012).

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It is worth noting that, telemarketing is still a common marketing method. Technological
advancements leading to a wide use of computers, mobile phones, internet, and social media has
created more avenues for marketing (Hardoon & Shmueli, 2015). The online marketing industry
has grown tremendously due since many people spend their time browsing the internet. Many
companies have seen this as an opportunity and invested heavily in online marketing through
these sites. The most recent development in the industry is the adoption of business analytics.
Recently, the world has witnessed great excitement around analytics. The aim of business
analytics is to assist companies collect, and analyze consumer data with the aim of generating
business insights that inform strategic decision making. The emergence of business analytics and
data science reflects the increased volume, variety, and velocity of data (Holsapple, Lee-Post &
Pakath, 2014). Many industries have now adopted the concept with the aim of increasing the
efficiency of operations and decisions. Business intelligence has diverse applications and thus
can be adopted in almost every sector of the economy. In marketing, business analytics would
boost the impact of marketing promotions, and marketing campaigns.
Drivers to adopt Business Intelligence
Business analytics in marketing involves the extensive utilization of data, qualitative and
quantitative techniques, explanatory, and predictive models in business decision making.
Business intelligence becomes useful when insights obtained through it are implemented.
Researchers are therefore concerned about transformation of organizations towards adopting
data-driven and evidence-backed decision making (Kohavi, Rothleder & Simoudis, 2012). The
marketing industry has faced significant challenges that has contributed to its adoption of
business analytics.

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Uncertainty in the business industry is a phenomenon where companies are unsure about
market movements since future events are uncertain. The ability of a firm to handle uncertainty
in the business environment determine its performance. Generally, consumer preferences and
economic landscape some markets change rapidly leading to fluctuation in demand of products
and services. Fluctuation in demand leads to unsatisfied demand when production is lower than
demand. A rise in demand above supply leads to over-production which as a result might trigger
perishability of goods, obsolescence, and low inventory turn-over. Data-driven decision making
through business analytics promote the creation of relatively accurate predictive models.
According to research findings, between 60-80% of customers do not purchase again
from the firms that satisfied them before. This is an indication of lack of customer loyalty which
is a marketing problem. It is desirable when firms obtain new clients while at the same time
retaining their previous customers. The marketing industry is interested in promoting customer
retention and loyalty so as to predict future growth. The aim of adopting business analytics in
marketing is to be able to boost customer retention. Business analytics captures customer
information and directs relevant advertisement to the appropriate prospective customers. Data
science techniques analyses consumer behavior and initiates appropriate advertising to the right
people. Accurate selection of target customers ensures that marketing strategies yield the
expected return.
Fast obsolescence of marketing strategies might render an organization’s marketing
method unproductive. This is contributed by changes in the marketing landscape and high
competition from competitors. Business analytics marketing uses historical and current
information and thus making marketing efficient. The adoption of business intelligence promotes

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faster response to changes in the business environment. Firms are interested in maximizing their
sales in order to boost their business profit and shareholder.
The need to make quick decisions in business decisions drives companies to adopt
business analytics. Businesses might lose profitable opportunities when decision making is
backed by manual procedures. Business analytics automates some procedures reducing time
spent in making decisions. Business analytics tools might propose decisions that should be can
undertake after considering market trends.
The advancement in technology in the recent years contributed to development of
analytic technologies. Statistical analytics soft wares such as SAS, SPSS, Stata, and Excel make
data analysis processes simpler and faster. Customer tracking technologies have also led to
advancement in business analytics. Marketing strategies can use available information to model
and predict customer preferences. Online marketing tools implemented on websites and blogs
lure customers in to purchasing products. Business analytics predicts customer preferences based
on customer purchasing history, demographics, or location. Marketing advertisements are then
projected on the website in form of pop ups offering the customer a deal.
Perceived Benefits from BA
The rise of the business analytics industry in the recent years has attracted huge attention
from industry practitioners and scholars. Many companies have adopted this technology
especially in their marketing departments with the expectation of making more sales. The use of
business analytics in marketing is perceived to create value since data can near accurately predict
future events. In marketing, business analytics helps firms predict consumer choices. Through

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analyzing and interpreting data, producers can identify trends in the market and therefore make
rational business decisions. When customers taste and preferences shift in favor of the producer
or marketers products, the producer responds by producing more of the product so as to meet the
demand. Also, a shift in customer taste and preferences in favor of a competitor leads the
producer in to producing less of the product. Data analytics might also indicate business factors
that significantly reduce or increase company performance. LaValle et.al (2011) reports that,
business performance is directly linked to the competitive impact of business intelligence. The
report also added that, top performing firms are highly likely to introduce business analytics
compared to small firms.
Business analytics enable the firm to segment its customer base in to groups depending
on their consumption characteristics. Marketing practitioner having skills in technology enables
them to apply business intelligence better. Segmentation is important since marketing managers
can customize or launch marketing campaigns differently according to segment (Laursen &
Thorlund, 2016). The manager can thereafter commit more resources to areas that are highly
economically viable. BA analytics technique also optimizes the market mix and therefore
enabling organizations to make achieve higher sales volume. Sales performance analysis
involves analyzing sales data to establish facts, areas that need to be changed, and competitive
factors. These analyses can be conducted more accurately using Business analytics tools.
Business analytics strategy employed by the industry
Analytics is the use of statistical methods, information systems applications, and research
methods to explore, visualize, and communicate trends in data. Analytics can be grouped in to
three categories as descriptive, predictive, and prescriptive analytics (LaValle, 2013). In the

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marketing industry, all the three techniques are appropriate. Descriptive analytics uses simple
statistical methods to describe variables of a dataset. Firms can analyze historical data in order to
appropriately segment its customers based on the value they have in business. The business can
then allocate time, resources, and efforts to create promotion strategies, create personalized
items, and lower product lines. The allocation will be based on product configuration of
segments rather than each consumer.
Predictive analytics uses advanced statistical techniques to construct predictive models
that show trends and associations. Through predictive analytics, variables that affect sales can be
determined. Regression models are the most common statistical predictive models employed in
marketing (Ragsdale, 2014). To predict a dependent variable, independent variables are obtained
and fed in to the model. When predictive analysis are conducted effectively, businesses can
retain their existing customers as well as obtain new one. Through business analytics, online
activities of users that include clicks and search items are collected and analyzed to determine
customer behavior. The obtained behaviors can then be used to create adds targeted on the
segments. Prescriptive models in business analytics apply decision science and research
methods concepts to optimize resources. Through linear programming, organizations can
determine the optimal mix of resources for marketing. Firms that have branches can use
prescriptive analytics to determine which branches to launch marketing campaigns.
Challenges to Business analytics strategy
The advancement in technology has led to production of large quantities data. Filtering
the data to remain with only relevant data is a challenge to data analytics. Organizations face
difficulties making use of these data. Synchronization of these datasets in to analytical tools is
also a major challenge to businesses (Stubbs, E., 2011).

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The BI strategy might also lack adequately skilled professionals. There is still a huge
demand for data scientists in the world, however has not been met by the supply. Acquisition of
these experts by organization is expensive making the cost of business intelligence high.
Organizations implementing the BI strategy should have an efficient data storage system.
Digital storage systems makes data easily accessible for analysis. However, with increased
cybercrime, companies BI might be insecure.
Actual benefits achieved through the implementation of Business analytics
Business analytics facilitate sales and operational planning that streamlines supply chain
and demand decisions in the organization. Through this, businesses estimate the quantity
demanded of their product given existing constraints and available data. Estimation of quantity
demanded and producing in line with the prediction lowers the possibility of adverse losses
especially while dealing with perishables (Taylor, 2011). It is worth noting that, predicted
amounts might fail to hold especially when there are market shocks. The collection of relevant
and accurate data and proper choice of analysis techniques promotes better decision making.
However, the use of poor data would make the all process invalid leading to generation of
invalid insights.
Business analytics improves decision making processes. The quality and relevance of
decisions made with the help of business analytics tools tend to be higher. This is because data-
driven decision making is based on hard data rather than intuition and observation. Data-driven
decisions are made through extrapolation of datasets. According to findings of research
conducted by MIT center of digital business, data driven decision making increases productivity

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by around 4% and profits by 6% (Weng and Lin, 2014). Business analytics also speeds up
decision making processes leading to timely action.
Business analytics is cost effective. The value generated from business analytics in
marketing is high compared to the cost of implementation. Aligning resources to marketing
strategy leads to more successful marketing campaigns. There are several marketing strategies
available for organizations. These include online marketing, social media marketing,
promotional campaigns, telemarketing, and bill boards. Business analytics in marketing tend to
work better with online marketing techniques although it can also be used to support traditional
marketing techniques. Business analytics can also be used to share data with third parties such as
supplies and customers.
Draw backs of the Selected Business Analytics Strategy
Business analytics is effective when data used is of good quality. Data analytics starts
with data collection and therefore, firms should ensure that they collect relevant and accurate
data (Sztandera, 2014). Data quality is essential for all business analytics strategies since the use
of poorly formatted or inaccurate datasets during analysis yields poor results and misleading
insights. Materially inaccurate data lead to poor predictions and prescriptions.
The creation of predictive models involves complex model building procedures. Small
enterprise might face difficulty actualizing implementing the strategies since they would have to
incur the high cost of acquiring a business analyst. The cost of installing business analytics tools
capable of actualizing descriptive, and predictive business analytics is expensive especially for
small companies (Kabir, N. and Carayannis, 2013).

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Recommendations
Business analytics involves making business decisions based on data. An effective
business analytics strategy requires accurate relevant data and precise analytics. Therefore,
organizations considering to use a business analytics strategy should create effective data
collection methods, effective analytics tools, right human resource, and infrastructure. The
recommendations below covers the process of building an effective business strategy.
The first step towards building an effective business intelligence strategy is creating a BI
road map. The road map captures all the areas that are of interest to the organization. It is a clear
strategy that indicates all the steps that should be implemented to make the BI strategy complete.
The road map should explain the reporting and analytics needs of the organization, industry Key
Performance Indicators (KPIs), customized KPIs, and clients. Understanding Industry KPIs
ensures that the implementers are knowledgeable about the BI needs of the industry since they act
as benchmarks. Customized KPIs are organization specific and are aligned with the company’s
business strategy and objectives. Additionally, understanding the needs of BI users would ensure
that the BI strategy implemented caters for their needs.
Organizations should also develop talents within the organization to support the business
analytics strategy selected. This would involve recruiting and developing people with diverse
skills that include an understanding of business analytics. The organization can also develop
continuous learning tools that support both internal and external clients. Identification of data
points across customer journeys also supports business analytics. The BI manager as the head of
the team should be have both business and technology skills. The organization should also have a
BI developer who designs and builds the technological infrastructure including data pipelines. The

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business analyst makes the process effective by analyzing and recommending business actions.
The business analyst acquires data and manipulates them in to productive form. The database
administrator creates and manages databases. Also, an effective BI strategy has data scientist who
uses programming skills to collect actionable insights from raw data
To implement an effective business strategy, the data sources should be identified and
harnessed. Marketing data comes from different a sources. The data should then be gathered and
organized to meet the strategic objectives of the organization. BI data include core, peripheral,
and external data. The BI strategy should filter the available data to ensure that they are relevant.
A data warehouse should also be created to store all available marketing data. This ensures that
the organization keeps its historical information for future use.
. Organizations should adopt a data-driven culture so as to build their databases with
marketing related data. Existing clients can be asked to provide their views concerning company
products and services and give feedback on how they can be enhanced. Analytics tools can also
be audited to prevent duplication and errors. Data-driven culture also promotes research
methodologies within the organization and thus improving decision making. The business
analytics strategy should also support the organization’s business strategy. Aligning business
analytics and business strategies ensures that it gains sponsor’s support from shareholders,
managers, and other stakeholders interested in growing the company’s wealth.
Innovation involves seeking new and effective solutions to problems. To effectively
implement a business analytics strategy, marketing personnel should be educated on creative
data use. They should be able to identify trends in marketing and create strategic decisions.
Innovation can be promoted by investing in research and development.

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References
Emblemsvåg, J., 2015. Business analytics: getting behind the numbers. International Journal of
Productivity and Performance Management, 54(1), pp.47-58.
Evans, J.R. and Lindner, C.H., 2012. Business analytics: the next frontier for decision
sciences. Decision Line, 43(2), pp.4-6.
Hardoon, D.R. and Shmueli, G., 2015. Getting started with business analytics: insightful
decision-making. CRC Press.
Holsapple, C.,d Lee-Post, A. and Pakath, R., 2014. A unified foundation for business
analytics. Decision Support Systems, 64, pp.130-141.
Kabir, N. and Carayannis, E., 2013, January. Big data, tacit knowledge and organizational
competitiveness. In Proceedings of the 10th International Conference on Intellectual Capital,
Knowledge Management and Organisational Learning: ICICKM (p. 220).
Kohavi, R., Rothleder, N.J. and Simoudis, E., 2002. Emerging trends in business
analytics. Communications of the ACM, 45(8), pp.45-48.
Kumar, V., 2015. Evolution of marketing as a discipline: What has happened and what to look
out for. Journal of Marketing, 79(1), pp.1-9.
Laursen, G.H. and Thorlund, J., 2016. Business analytics for managers: Taking business
intelligence beyond reporting. John Wiley & Sons.
LaValle, S., Hopkins, M.S., Lesser, E., Shockley, R. and Kruschwitz, N., 2013. Analytics: The
new path to value. MIT Sloan Management Review, 52(1), pp.1-25.