Strategy for Effective Integration of Analytics in Startup

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Strategy for Effective Integration of Analytics in Startup


The current paper provides a memo, which will demonstrate the way to develop a strategy for effective integration of analytics and analytics professionals into organizational decision-making. As the company under analysis represents a startup that operates in the advertising industry, the dynamic environment stimulates ‘speed and tempo’ to be the main drivers of the decision-making process that makes the company focus on decisions of low influence regarding the company’s future. Therefore, the company ignores strategic decisions, as they are not significant for the state of the current company. In addition, , the startup is mainly guided by gut decisions that arise from a solid strategic and consensus decision making, as the company  is at the early stage of its development. The company has to change the focus to evidence-based and data-grounded decisions, which will assist it in avoiding biased judgments. The implementation of analytics can assist the company in redesigning its processes to use data in order to define complex business process and interdependences, predict company’s future, segment customer population, and analyze customer requirements. The current paper will recommend a complex six-step strategy, which will help in implementing analytics in the decision-making process of the company. The strategy will result in value realization that will shift the company from the gut-grounded mindset to the data-grounded one and to outcomes-grounded mindset later.

Key words: analytics, strategy for implementing analytics, startup, advertising, customer segmentation

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Strategy for Effective Integration of Analytics in Startup

Human Resource managers make numerous decisions on a daily basis by reacting to specific situations. The more background data is available the better is the cognitive capability of making a good decision from a wide diapason of possibilities. The analytics stands for getting a computer system execute the analogous processes. In fact, it is a well-known fact that analytics and “big data” can assist businesses in becoming smarter, more efficient, productive, and improve prediction making. In fact, analytics defines the discrepancy between the industry losers and winners. The integration of analytics is a long journey even under the most supportive and sustaining circumstances, because it might require a lot of time for data gathering, integration of the relevant technologies, building the required skills, and embedding analytical decision-making into principal organizational operations and processes. In fact, the current world is characterized by the explosion of newer data types, which can be retrieved from all sorts of devices, channels and organizations. Thus, startups, which are supposed to operate in a highly competitive environment, should effectively use this information, as it can provide them with valuable insights. Actually, this is the main reason why analytics has to be viewed as a form of the extensive usage of both qualitative and statistical data, implementing predictive and explanatory models to stimulate fact-grounded business management decisions and operations. In case of the current organization, which operates in the advertising industry, analytics can assist in optimizing major functions, processes, and roles. Moreover, analytics will enable organization to meet stakeholder reporting requirements and demands, manage enormous data volumes, create market advantage, manage risk, enhance controls and, eventually, develop organizational performance by turning information into intelligence.

Company Analysis and Role of Analytics

The analyzed organization is a startup company in advertising industry. In fact, the analyzed startup represents a small company that consists of four founders and fifteen employees. Due to the fact that the company is forward-looking and it operates in a highly dynamic environment, it has to define new sources of value that exist in an organization and the ways how they can be exploited in order to cultivate future opportunities for value creation and protection. Thus, the case of this company is a good example, when analytics can provide a company with a possibility to increase, optimize, and secure its value efficiently (Nutt & Wilson, 2010). In the case of the current company, analytics can be viewed as an indispensable implement needed for creating value in an advertising industry. Actually, analytics will help the company in equipping an all-around perspective of existing market circumstance, customer requirements and preferences, and possible startup risks. In fact, in the case of the company under discussion, analytics might become helpful in eliminating dependence on so-called “gut feel” decision-making, which is currently prevailing in the company, as this is a stage when instinct following appears to be the most valuable and supposedly saves time (Yakowicz, 2015). On the one hand, it helps with stopping second-guessing, but on the other hand, it might be too risky because it is unsupported by data and insights. Thus, firstly, the company and its HRM will be capable of understanding and embracing the emerging possibilities and aligning its services according to the altering customer requirements that will generate supplementary value for all the stakeholders. Secondly, analytics will be helpful in finding and measuring intangible value sources in a more effective and efficient manner, as it will assist in balancing and relating hard data from the balance sheet to a variety of qualitative evidence, including employee capabilities, customer sentiment, service innovations, etc. Thus, implementation of analytics will help the company in getting a more comprehensive cognition of valuation drivers, while equipping clear solutions regarding value management, its connection and communication to a broad scope of stakeholders, the market, and industry at the same time.

Drivers of Decisions Making and Strategies for Analytics Professionals

Before developing a specific strategy for effective integration of analytics into organizational decision-making, it is important to understand the drivers of decision making in the analyzed organization, to review the existing strategies by analytics professionals in order to engage in effective decision-making and to demonstrate professional competence and expertise. The facts demonstrate that there are three major challenges connected to integrating analytics in decisions making. The first one concerns delineating and arranging data, which has to be used. The second one stands for managing analytics at the same time securing the relevant capabilities to sustain the entire process. The third one regards the insights utilization, as they should be used to change the organizational operations and decision making of the company. In fact, good decision making depends on attributing and appointing understandable, comprehensible roles and functions. Despite the fact that this actually sounds easy, a lot of companies, especially startups struggle with this part, as either many people feel responsible for the same or no one does (Rogers & Blenko, 2006). The analysis of the current company demonstrates that it concentrates on decisions, which appear to have low influence on the company’s future. The company depends on ‘speed and tempo’ that represent the main drivers of its decision-making. In fact, the main reason, why the strategic decisions are ignored, is the fact that they appear to have low current importance. On the one hand, service features can be viewed as the most significant driver of decision-making. On the other hand, the focus on service features makes the company neglect the necessity of developing appropriate competences in personnel, as it is insignificant for the current startup phase. Moreover, the startup is directed mostly by gut decisions that arise from a solid strategic vision, as the company is very young. Currently, this driver helps the company in delivering stronger offering. Nevertheless, this driver should be changed to be data-driven not merely gut-driven, as there will be no possibility of enhancing productivity, if the company cannot measure it.  Therefore, the facts demonstrate that the current startup solidly depends on gut-feel and consensus decision-making. The company has to change the focus to evidence-based decisions, which will help in avoiding biased judgments.

Analytics will help the company in using data and outlining complicated business processes and interdependences, at the same time assisting in predicting organizational future through the customer requirements analysis. What is more important, analytics will help in segmenting customer population for targeted market. Due to the fact that the company has limited resources, analytics will assist in prioritizing the customer segments that should be targeted with marketing efforts. The customer segment realm can help in driving the rest of the company’s development. Analytics will help in narrowing the characteristics of the prototypical customer. The company should serve defined customer segment, operate more integrated inner functions and supply chains, run consistent processes and extract of its resources (Hall, 2013). Data decision-making grounded on analytics will help in breaking customers down into segments, which will appear to be the most promising. Four basic methods of segmenting customers are typically used and they are subdivided into demographic, geographic, psychographic, and behavioral. However, the current case demonstrates that it is important to utilize a combination of four methods mentioned above in order to define a subset of all the customers, who are fit the services of the company the best.    

In fact, there are six major soft skills, which should be taken into account and used by analytics professionals in order to engage in effective decision-making and to demonstrate their competence and expertise that encompass working with teams, problem framing, collecting data from groups, communicating results, partnering with clients, and interviewing experts (Hill, 2011). Firstly, as the current company is a startup organization in advertising industry, it is highly important to gain an understanding how to turn clients into partners. In fact, clients appear to be the final consumers of organizational services and work. Therefore, it is necessary to develop a winning connections and relationships with clients in order to omit possible conflicts (Azzarello, 2015). In case a client turns into a champion, the company obtains a possibility to transform its consumers into partners. Secondly, the current startup does not have vivid organizational teams, which obviously deteriorates the decision-making process. Moreover, the current situation demonstrates that team members do not have enough confidence to express their view regarding some decisions, which results in false harmony and shallow agreements. Such a situation specifically concerns some important decisions. As the working teams do not have a tendency to argue openly and share their disagreements during private discussions, the company appears to miss the most valuable insights the debates and issues discussion can provide (Azzarello, 2015). Actually, this situation solidly deters the company’s progress (Azzarello, 2015). Currently, people, who work in the analyzed startup, appear to have different skills and approaches. The company can solidly benefit from these differences. However, this is the case, when the project teams should be carefully planned, as the team should be concentrated on the issue while managing conflicts. Moreover, the company will be capable of finding a healthy balance between convergent and divergent thinking (Azzarello, 2015). Thirdly, the analysis demonstrates that the company has issues with framing a problem sometimes. In fact, there are situations, when stakeholders are neglected despite the fact that they represent the most significant constituencies, which affect the company’s operations. Thus, this is a reason, why HR should discuss the issues with stakeholders, build a specific decision framework and generate a value hierarchy that will assist in comprehending and managing the expectations of existing stakeholders.

Integrating Analytics in the Company

The cornerstone of the strategy stands for the data-driven culture, as it is a principal constituent of the successful integration of analytics into the organizational decision-making process. It practically means that big-data analysis has to become a specific type of company’s organizational function. Actually, this means that all the operations have to be analyzed and estimated from an analytical viewpoint by taking into account the data quality, amount, and implications. HR and stakeholder should represent the advocates of big-data analytics, as they are the people, who can influence and guide functional teams and stimulate them to use data for generating actionable insights. The company should employ data analytics professionals, who will be integrated into the team in order to analyze and estimate the processes and ensure that necessary data is delineated and induced at each stage of the organizational business processes. In addition, it is also important to present all the outcomes in a form, which speaks the language of its audience. Therefore, the company should start using specific visualization techniques.

The resources stand for the second crucial constituent. In fact, the resources incorporate right people and right implements. Qualified analytics and data professionals appear to be a precondition of felicitous implementation of analytics across the company’s team. In addition, successful integration of analytics deeper in decision-making relies on centralization and decentralization of resources. Such a technique will help in providing a balanced significance to employees, implements, and services (Rogers & Blenko, 2006). The current organization places greater importance on implements and services in order to satisfy its customers than on employee competencies and skills, which will have more solid importance for the future decision-making and productivity of the company.

The third constituent of the strategy stands for the process, which concentrates on quality, consistency, and storage of data that is accompanied by the generation of sound analytical models. Moreover, this constituent concerns the utilization and application of data-driven strategies. The process of developing analytical capabilities and utilizing the analytic insights in strategic decision-making cannot be viewed as a simple task. In fact, HR can increase and enhance analytic capabilities by investing in four major spheres that involve data-savvy professionals, quality data, analytical tools and processes, and inducements, which sustain analytical decision-making. There is a requirement to create and develop analytical and predictive models. However, it is also necessary to measure the business influence of these models in order to change them if needed.

The final constituent stands for the governance. The major aim of this constituent involves ensuring smooth operation of the analytical professionals together with the adjustment of analytical operations, which are embedded in the startup business operations (Rogers & Blenko, 2006). Moreover, this constituent will help in making long-term decisions by ensuring transparency and accountability in the analytical function at the same time.

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Recommended Data-Driven Strategy for Implementing Analytics in the Company’s Decision Making

There are six major steps in the creation of an effective data-driven strategy of the decision-making in the analyzed startup. Firstly, it is important to define the key performance indicators. These key performance indicators drive the decisions of the company. The company should understand whether it focuses merely on revenue or it is also interested in creating great customer experience, acquiring new customer and re-engaging current customer in various up-sell possibilities. In fact, key performance indicators are closely connected to the existing business aims and objectives of the startup. Clear identification of these aims and objectives will help in defining key performance indicators and enhancing the appropriate decision-making.

Secondly, it is important to define the data, which is necessary for measuring key performance indicators. Gathering of relevant input and provision of appropriate data and analysis allows making sensible decisions in a timely fashion (Roger & Blenko, 2006). On the other hand, wrong data might direct (and typically directs) the company focus on inappropriately segmented customer groups. Actually, this might provide completely distorted perspective of the customer conduct and preferences. Moreover, it is necessary to group the required data into qualitative and quantitative. This will help in guiding the organizational decision-making process in the appropriate direction, as the company might require knowing the buying habits of the customer, on the one hand, while it might be necessary to obtain knowledge of more personal data, on the other hand. Analytics will direct the startup to the appropriate decision-making process, which will provide the company with a correct view of the consumers and targeted segments. The whole process will also help in simplifying the decision-making process.

The third step concerns data collection methods. Actually, it is important to take into account the fact that data should be collected in a non-intrusive way. Despite the fact that the company has an ability and possibility to gather a great amount of consumer data to develop targeted segments, customers appear to be less patient regarding user privacy encroaching. In order to omit the creation of a negative customer experience, the company should use both first and the third party sources to collect necessary data. The fact that data collection process is incessant presupposes that it has to alter and evolve simultaneously with customers’ habits, which depend on emerging trends, technology and personal alterations.

The fourth step stands for data analysis and creation of customer personas. In fact, the main objective of data collection stands for provision of meaningful analytical insights, which will drive future decision-making process in the company. The gathered information cannot formulate any decision strategies. Nevertheless, its usage will help in procuring insights, which will guide the overall decisions and approaches formulation (Hill, 2011). This step incorporates analysis of customer behavior, buying patterns, preferences and backgrounds, as it helps in segmenting targeted customers and developing discrepant buyer personas. This information can assist in enhancing decision-making, which is specifically focused on customers.

Fifthly, it is important to create customer-concentrated advertising content. When all the necessary information has been collected and analyzed, the decisions can be specifically oriented on peculiar customer groups. In fact, this will show the customers that the company knows them, cares about them, and can delight them with greatly individualized offers. This will stimulate customers to come back to the company and select it among all the other existing competitors in the dynamic industry of advertising (Hill, 2011).

Finally, it is important to measure the ROI (return on investment). Such a technique will demonstrate the channels and customer segments that are generating the major part of the return, the most responding segments to the company’s efforts, and the most efficient previous decision-making processes. This will help in customizing the decision-making process for each targeted segment on the basis of the traffic it provides. The company should also remember that it is necessary to control and monitor the decision-making process based on analytics in order to enhance it in accordance with the latest insights.

Recommendations and Conclusion

The implementation process demonstrates that integration of analytics philosophy in organizational decision-making requires analyzing business issues, outlining most appropriate data and analysis, and reengineering decisions in order to provide the fundamental ground for utilizing the obtained insights. Actually, these insights can deliver huge value for the current startup and provide the closest positioning to the company’s organizational decision-making. The most crucial constituent of the process stands for the capability of effective management of supply and demand needed for integration of analytics services across the company. The current case demonstrates that it is more important to adapt cross-functional operations, activities, functions, and responsibilities to integrate analytics into daily organizational decision making than just bolt analytics into the processes and operations of the startup. This will help the company to generate higher return on analytics capacities and institutionalize its usage in everyday decisions at the same time. Due to the fact that alterations for enhanced performance require fact-grounded and data-based discussions and decisions, the recommended strategy for implementing analytics in the company’s decision-making is a data-driven one. This will require analyzing and defining the insights in order for them to be timely, actionable, and relevant. Nevertheless, the company will also need to perform some changes, which will increase the value and visibility of implemented analytics. They will incorporate consistent and deliberate connection of all the other organizational strategies and tactics to the data and insights generated from analytics, which has to be combined with prioritizing of outcome-grounded analytical requests, which are closely clearly connected to meeting significant business objectives of the enterprise. 


The main problem and challenge for the startup companies is the fact that these companies launch and integrate analytics or dedicate ‘big data’ efforts while having no clear view of what they actually want to accomplish that results in solutions that are not connected to existing business issues. The current case stands out, as the company clearly understands that the major role of analytics will represent segmentation of customer populations. The recommended strategy, which concerns the role of analytics mentioned above, will result in value realization that will help the organization to shift from the data-grounded mindset, which will be formulated by the strategy, to outcomes-grounded mindset that will assist in minimizing needless or unproductive request for analytics support and sustainment.  

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