Market intelligence

MI and its broader term, marketing intelligence, was first introduced in “Marketing Intelligence for Top Management” by Kelley,{{Cite journal|last=Kelley|first=William T.|date=1965-10-01|title=Marketing Intelligence for Top Management|url=https://doi.org/10.1177/002224296502900405|journal=Journal of Marketing|language=en|volume=29|issue=4|pages=19–24|doi=10.1177/002224296502900405|s2cid=168050731|issn=0022-2429|url-access=subscription}} to provide information that was analyzed, reliable and consistent for an organization to better create policies and make business decisions.

Following Kelley, in “How to Develop a Marketing Intelligence System”, R. Pinkerton shows the proactiveness of organizations as marketing intelligence systems is applied whilst the technological revolution arises.{{Cite journal|last1=Kumar Vishnoi|first1=Sushant|last2=Bagga|first2=Teena|date=2020-03-28|title=Marketing Intelligence: Antecedents and Consequences|url=https://papers.ssrn.com/abstract=3563107|language=en|location=Rochester, NY|doi=10.2139/ssrn.3563107|ssrn=3563107|s2cid=229598952|url-access=subscription}} Contributions to MI include professional organizations such as “Global Intelligence Alliance” and “the Society of Competitive Intelligence Professionals” (SCIP). These organizations have contributed both empirical and theoretical research in an attempt to further define and understand MI.{{Citation|last=Egan|first=Michelle P.|title=Conclusion: Governance and Market-Building|date=2001-06-14|url=http://dx.doi.org/10.1093/0199244057.003.0011|work=Constructing a European Market|pages=260–272|publisher=Oxford University Press|doi=10.1093/0199244057.003.0011|isbn=978-0-19-924405-8|access-date=2021-05-31|url-access=subscription}}

As research into MI comes from scholars and non-scholars of different backgrounds it has resulted in a fragmented state of research. This has led to MI being used interchangeably with other market terms such as competitive intelligence, business intelligence and strategic intelligence. MI to this current date continues to change to meet organizational requirements.

The implementation of MI varies depending on how organizations perceive it. MI is defined as being composed of three main activities, these activities are Information Acquisition, the gathering of marketing information that is required for current and future customer needs, Information Analysis which is the intelligence gained from the information collected and Information Activation, which is using the intelligence to implement and develop marketing plans.{{Cite journal|last1=Kumar Vishnoi|first1=Sushant|last2=Bagga|first2=Teena|date=2020|title=Marketing Intelligence: Antecedents and Consequences|url=https://www.ssrn.com/abstract=3563107|journal=SSRN Electronic Journal|language=en|pages=2|doi=10.2139/ssrn.3563107|s2cid=229598952|issn=1556-5068|url-access=subscription}}

Frameworks can be flexible, however the basis that organizations use to model the MI surrounds a four-step process, which are, collection, validation, processing and communication. Data mining techniques are used throughout the processes to aid in the gathering and analyzing of data and information retrieved.{{citation needed|date=December 2021}} MI is a continuous process that organizations need to keep track of to improve their strategic and tactical marketing planning.{{cite web |last1=Fuller |first1=Connie J. |title=What is Tactical Marketing? |url=https://www.marketing-tip.com/what-is-tactical-marketing-definition-examples-plan/ |website=Marketing |date=6 January 2023 |access-date=6 January 2023}} These processes target the three activities that MI is defined by. The model can be adjusted and adapted when required and can be implemented all at once or by sections.{{Cite journal|date=2013-01-01|title=Approaching Market Intelligence Concept through a Case Analysis: Continuous Knowledge for Marketing Strategic Management and its Complementarity to Competitive Intelligence|journal=Procedia Technology|language=en|volume=9|pages=463–472|doi=10.1016/j.protcy.2013.12.051|issn=2212-0173|last1=Jamil|first1=George Leal|doi-access=free}}

Collection is the first step in the MI model, it involves the gathering of data and information of a particular market sector. Such data and information can be gathered from external sources, such as other organisations and their market strategies, research institutes and business reports.

Internal factors can include looking into current strategy processes and personal customer trends. It is estimated that 70% to 80% of intelligence resides within organizations employees or, internal MI network, as they are the team who gains information's when interacting with suppliers, customers and other industry contacts.{{Cite journal|last1=Tsu Wee Tan|first1=Thomas|last2=Ahmed|first2=Zafar U.|date=1999|title=Managing market intelligence: an Asian marketing research perspective|url=https://www.emerald.com/insight/content/doi/10.1108/02634509910293124/full/html|journal=Marketing Intelligence & Planning|language=en|volume=17|issue=6|pages=39|doi=10.1108/02634509910293124|issn=0263-4503|url-access=subscription}} To involve employees into an intelligence program to gain data and information the following considerations can be noted: developing a rewards program to promote participation, providing MI goals, requirements and a timeframe for information to be given in and creating a proper communication method to promote the intelligence program with employees such as using an e-mail system.{{Cite journal|last1=Tsu Wee Tan|first1=Thomas|last2=Ahmed|first2=Zafar U.|date=1999|title=Managing market intelligence: an Asian marketing research perspective|url=http://dx.doi.org/10.1108/02634509910293124|journal=Marketing Intelligence & Planning|volume=17|issue=6|pages=302|doi=10.1108/02634509910293124|issn=0263-4503|url-access=subscription}}{{Cite book|last=Gordon|first=Ian, June 19-|url=https://www.worldcat.org/oclc/19125425|title=Beat the competition : how to use competitive intelligence to develop winning business strategies|date=1989|publisher=B. Blackwell|isbn=0-631-15991-6|location=Oxford, UK|oclc=19125425}}

A challenge that arises in the collection of data and information is the identification of relevant information, this is a result of organizations not clearly defining a market sector.{{Citation needed|date=December 2021}}

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Validation is the second step in the MI model, this which can be referred to as data cleansing. The maintenance of good data quality is important as data and information is being retrieved from many different sources.{{Cite journal|date=2019-01-01|title=A Review on Data Cleansing Methods for Big Data|journal=Procedia Computer Science|language=en|volume=161|pages=731–738|doi=10.1016/j.procs.2019.11.177|issn=1877-0509|doi-access=free|last1=Ridzuan|first1=Fakhitah|last2=Wan Zainon|first2=Wan Mohd Nazmee}} Data and information obtained from sources can be dirty, meaning that it is incomplete, wrong, inappropriate, duplicated. This step will allow data and information to be adjusted and understandable to the organization, furthermore it allows for consistency and compliance to be present. If data quality is not maintained correctly it can lead to organizational losses with revenue and governmental regulation failures.

Data cleansing is a complex process that involves several stages in order to get good data quality for MI strategy use. Stages include defining the organization's level of data quality, detecting error from the data collected and then repairing the errors.{{Cite journal|date=2019-01-01|title=A Review on Data Cleansing Methods for Big Data|journal=Procedia Computer Science|language=en|volume=161|pages=732|doi=10.1016/j.procs.2019.11.177|issn=1877-0509|doi-access=free|last1=Ridzuan|first1=Fakhitah|last2=Wan Zainon|first2=Wan Mohd Nazmee}} The five stages of data cleansing are data analysis to identify errors, eliminating the errors, checking to ensure elimination of error are done appropriately, refreshing the data in the data warehouse and finally replacing the dirty data with clean data.{{Cite journal|date=2019-01-01|title=A Review on Data Cleansing Methods for Big Data|journal=Procedia Computer Science|language=en|volume=161|pages=734|doi=10.1016/j.procs.2019.11.177|issn=1877-0509|doi-access=free|last1=Ridzuan|first1=Fakhitah|last2=Wan Zainon|first2=Wan Mohd Nazmee}}

Processing is the third step in the MI model. It involves the use of translating the clean data using organizational rules, modelling, logic and analysis to produce readable information, reports and spreadsheets that allows the organization to gain specified knowledge. The interpretation of data into readable information is difficult as it is complex, it requires proper technology and heavy commitment from a top organizational level to match data and information gained and align it to a marketing strategy.{{Cite journal|last=Fleisher|first=Craig|date=1990|title=The competitive analysis of non-market intelligence|url=http://dx.doi.org/10.1002/cir.3880010206|journal=Competitive Intelligence Review|volume=1|issue=2|pages=11–13|doi=10.1002/cir.3880010206|issn=1058-0247|url-access=subscription}}

Communication is the last step in the MI model. It involves the sharing, delivering and transmission of information gained from the processing step to figures in the organization who will apply it accordingly to the market strategy.{{Cite book|last=Heang|first=Rasmey|url=http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-33508|title=BOOK REVIEW: THE USE OF MARKET INTELLIGENCEIN COMPETITIVE ANALYSIS|date=2017|publisher=Digitala Vetenskapliga Arkivet|pages=8}} As MI is a continuously changing, the communication of the MI strategy requires managers whom have expertise in the given market industry in order to determine the ongoing validity of the MI strategy and its implementation.{{Cite book|last=Heang|first=Rasmey|url=http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-33508|title=BOOK REVIEW: THE USE OF MARKET INTELLIGENCEIN COMPETITIVE ANALYSIS|publisher=Digitala Vetenskapliga Arkivet|year=2017|pages=48}} In order to make the communication of the MI strategy as successful as possible, this process must be performed by every level of an organization, also known as the intelligence organization.

Intelligence organization refers to the “people and information resources who make the market intelligence process happen”. The five elements of an intelligence organization are, MI leadership who manages and leads the MI process, a MI team, a portfolio of external information sources that is set up by the MI team, internal MI network made up of MI users and the MI user's personal information source network. An intelligence organization element is made up of external and internal factors that allows for a continuous MI process.

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The gathering of MI data is different dependent on an organization's financial capabilities. Sources of data and information is separated into qualitative, quantitative, formal, informal, published and unpublished. With such sources being retrieved both internally and externally from the organization. It involves using search engines and corporate web sites to see competitor's strategies, identifying business trends through reputable publications and existing customer clientele.{{Cite journal|date=1991|title=SCIP Europe established|url=http://dx.doi.org/10.1002/cir.3880020129|journal=Competitive Intelligence Review|volume=2|issue=1|pages=51–52|doi=10.1002/cir.3880020129|issn=1058-0247|url-access=subscription}} Organizations use different systems to gather MI, one system is that is used is Open-source Intelligence system.{{Cite journal|last=Sharma|first=Arun|date=2020-07-17|title=The organization of customer support services|journal=European Journal of Marketing|volume=54|issue=7|pages=1813–1814|doi=10.1108/ejm-07-2020-974|issn=0309-0566|doi-access=free}}

Sources of internal intelligence gathering include but are not limited to, gathering data from customers, manufacturers, through research and development (R&D), employees, also known as salesforce, physical evidence, sales quotes, sales records, trade shows and new hires.{{Cite journal|last1=Lackman|first1=Conway|last2=Saban|first2=Kenneth|last3=Lanasa|first3=John|date=2000-02-01|title=The contribution of market intelligence to tactical and strategic business decisions|url=http://dx.doi.org/10.1108/02634500010308530|journal=Marketing Intelligence & Planning|volume=18|issue=1|pages=8|doi=10.1108/02634500010308530|issn=0263-4503|url-access=subscription}} These data sources were ranked by organizations on a scale measuring five for being very important to one being not important. It was founded that customers and manufacturers and R&D are the most important to organizations with one hundred percent of organizations ranking these data sources with the number four or higher{{Cite journal|last1=Lackman|first1=Conway|last2=Saban|first2=Kenneth|last3=Lanasa|first3=John|date=2000-02-01|title=The contribution of market intelligence to tactical and strategic business decisions|url=http://dx.doi.org/10.1108/02634500010308530|journal=Marketing Intelligence & Planning|volume=18|issue=1|pages=7|doi=10.1108/02634500010308530|issn=0263-4503|url-access=subscription}}^,. It shows that in the process of collection and gathering MI data and information, these data sources brought the most value to organizations.

Sources of external intelligence gathering is included but is not limited to, gathering data from client meetings, dealers/distributors, customers, business associates, market research projects, suppliers, online services, periodicals and government publications. These data sources were compared on the same scale as internal intelligence gathering sources, with results showing that intelligence gathered through client meetings being the most important to organizations, with one hundred percent of organizations ranking this data source with the number four or higher.

Marketing information systems allow for organizations to continuously acquire, generate, and maintain external and internal information.{{Cite journal|last1=Kumar Vishnoi|first1=Sushant|last2=Bagga|first2=Teena|date=2020|title=Marketing Intelligence: Antecedents and Consequences|url=https://www.ssrn.com/abstract=3563107|journal=SSRN Electronic Journal|language=en|pages=4|doi=10.2139/ssrn.3563107|s2cid=229598952|issn=1556-5068|url-access=subscription}} They are systems that make use of artificial intelligence (AI) technology to aid in the planning of strategic and tactical marketing strategy of MI but also share marketing expertise.

Open-source intelligence is a predominant form of MI gathering that organizations employ. OSINT is defined as the scanning, finding, gathering, exploitation, validation, analysis, and sharing with intelligence-seeking clients of publicly available print and digital/electronic data from unclassified, non-secret, and grey literature.{{Cite journal|last=Fleisher|first=Craig S.|date=2008-07-25|editor-last=Calof|editor-first=Jonathan L.|title=Using open source data in developing competitive and marketing intelligence|url=https://www.emerald.com/insight/content/doi/10.1108/03090560810877196/full/html|journal=European Journal of Marketing|language=en|volume=42|issue=7/8|pages=853|doi=10.1108/03090560810877196|issn=0309-0566|url-access=subscription}} It is frequently used as its system is user friendly, its inexpensive and that it processes an abundant amount of raw materials that can be further processed.

Using MI can bring to organizations both benefits and issues depending on how MI is acquired, maintained, and implemented. Benefits that MI can bring includes but is not limited to gaining competitive advantage in their marketing strategies. Issues that MI can bring can include but is not limited to, financial losses and government regulatory failures.

There are issues that arises in the process of acquiring MI data and information and the implementation of an organizations marketing strategy. Issues such as the acquiring intelligence unethically and illegally can lead to failures with government regulations, also, if dirty data is not properly cleansed and problems aren't mitigated or resolved can lead to a range of negative impacts that can result in financial and reputational losses to the organization.

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An issue that can arise is the unethical and illegal collection of data and information. Organizations can collect data for MI illegally or unethically to try to gain competitive advantages; this is known as industrial espionage.{{Cite journal|last=Button|first=Mark|date=2020-03-01|title=Editorial: economic and industrial espionage|journal=Security Journal|language=en|volume=33|issue=1|pages=2|doi=10.1057/s41284-019-00195-5|issn=1743-4645|doi-access=free}} An example of illegal MI collection practice is when British Airways breached the Data Protect Act 1984 through accessing Virgin's confidential flight details.{{Cite web|date=2011-10-22|title=Battle of the Airlines: Computer hacking of flight details 'was|url=https://www.independent.co.uk/news/uk/battle-airlines-computer-hacking-flight-details-was-illegal-british-airways-ran-complex-covert-operation-steal-customers-richard-branson-s-virgin-atlantic-1478009.html|access-date=2021-05-19|website=The Independent|language=en}}

A standard of conduct was developed by the non-for-profit organization Society of Competitive Intelligence Professionals, creating a code of ethics that can be adhered to by organizations when collecting market intelligence, to prevent the illegal and unethical collection of data and information.

Dirty data that is collected needs to be cleansed to maintain good data quality. Challenges that arise in data cleansing is that there is a large volume of data being received leading to organizations being faced with many risks of failure to detect dirty data being processed through.{{Cite journal|last1=Amaravadi|first1=Chandra S.|last2=Samaddar|first2=Subhashish|last3=Dutta|first3=Siddhartha|date=1995|title=Intelligent marketing information systems:: computerized intelligence for marketing decision making|url=https://www.emerald.com/insight/content/doi/10.1108/02634509510083464/full/html|journal=Marketing Intelligence & Planning|language=en|volume=13|issue=2|pages=734|doi=10.1108/02634509510083464|issn=0263-4503|url-access=subscription}} If data quality is not managed properly, it can result in financial losses, inefficient implementation of MI strategies and failure to comply with government regulations.{{Cite journal|last=Redman|first=Thomas C.|date=1998|title=The impact of poor data quality on the typical enterprise|journal=Communications of the ACM|language=en|volume=41|issue=2|pages=80|doi=10.1145/269012.269025|s2cid=17205136|issn=0001-0782|doi-access=free}} A reason for financial loss is due operational costs, as there is an increase in resources and time spent to identify and fix the dirty data.{{Cite journal|last=Redman|first=Thomas C.|date=1998|title=The impact of poor data quality on the typical enterprise|journal=Communications of the ACM|language=en|volume=41|issue=2|pages=81|doi=10.1145/269012.269025|s2cid=17205136|issn=0001-0782|doi-access=free}}

MI processes have been used in many organization's strategic market planning, however, there are still difficulties in what the hard and soft benefits in using a MI process for an organization. The benefits of a successful MI process can be sectioned into three categories, better and faster decisions, time and cost savings and organizational learning and new ideas, however, overall, it can improve profitability and the competitiveness of an organization. The competitiveness of an organization increases as with more MI gathered it'll provide a way for organizations to innovate through improving current methods and increasing the ability to find and create new products.{{Cite web|title=[EBOOK] Market Intelligence: Stand out with Data Monitoring|url=https://resources.rockcontent.com/market-intelligence|access-date=2021-06-02|website=resources.rockcontent.com|language=en}}

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