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). 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.
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.
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 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.
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 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.
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 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).
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.
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 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.
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).
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 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.
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.
Ragsdale, C., 2014. Spreadsheet modeling and decision analysis: A practical introduction to business analytics. Nelson Education.
Stubbs, E., 2011. The value of business analytics: Identifying the path to profitability (Vol. 43). John Wiley & Sons.
Sztandera, L., 2014. Computational intelligence in business analytics: Concepts, methods, and tools for big data applications. Pearson Education.
Taylor, J., 2011. Decision management systems: a practical guide to using business rules and predictive analytics. Pearson Education.
Weng, W.H. and Lin, W.T., 2014. Development trends and strategy planning in big data industry. Contemporary Management Research, 10(3).
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