Tesco Club Card for Information Gathering

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Introduction:

I will be addressing the importance of IS and why it’s crucial that process of gathering data and transform it into information and knowledge is done on a high standard in today’s world for modern businesses, I will then apply this process to Tesco club card and describe their strategic activities carried out.

Data refers to the fundamental summary of things documented such as transactions, activities and events. These are classified and stored as, non-random figures, words, facts gathered in research and documented plus collected non-random data and record of an event, they have no use to the reviewer because they only consist of raw facts such as numbers, letters and also sounds or images. An example of data is grade achieved by a student in a class or how many hours an employee has worked within a week. (Rainer and Turban, 2008:8) On the other hand Bocij et al., (2008) states that data has no value because it needs to be processed and transformed in order to get meaning, then it will turn it into information, data are raw and in most cases, they usually have no meaning/value.

Datum is single piece of data. Term noise is used when gathering data to describe unrelated items of data which are considered to be essential without meaning. Data placed with frame of reference/context will give a meaning to a manager.

Chaffey and White (2011) assert that data is discontinuous, straight facts about events. Data are converted into information when value has been added through context, calculations, corrections and consolidations.(Dawson, 2015) Agrees with most of the authors but he also adds that raw data gathered essentially means nothing and they are sometimes referred to as intelligence gathering, and it is important for a research study, in true words it is not considered as research. On the other hand (Jessup and Valacich, 2007) mentions before understanding how information systems work, analysis of differences between data and information is required, data is raw unprocessed, classed as numbers/words. Number/figure 20 has no meaning/context, until as an example it is present as 21% sugar, now the number has context and we might assume it is a dessert recipes. Data can also be sizes, different colours , weights and dates and like many other authors mention they may not bring information to a reader (Boddy et al., 2009)

One data characteristic is data storage; enabling data being stored safely e.g. data warehouse, which concentrates for management reporting/analysis. This database has additions of fresh and aged data obtained from operational systems. (Laudon and Laudon, 2018)

Data quality is another characteristic, which is essential for precisely represent data from the real world for example from an event,  Data then will become valuable information, data is required to be authentic, this comes from correct interpretations of events. It will be concluded when all data is present, valid/consistent so it becomes high quality.(Chaffey and White, 2011)

Quantitative/hard data manages to use statistics which are considered as figures, its purpose is to measure/evaluate object/situation e.g. Shoe size. In contrast, soft/qualitative data illustrates the qualities of a situation, e.g.an interview collecting opinions of a person

Information is once a data is processed and now it has a context/meaning, these processed data have a purpose. Data understood by a recipient is information.(Bocij et al., 2008)

Information used will make effective decisions, firstly collecting the actual data, applying it to a transformation process to create information; this process needs to be compelling. (Chaffey and White, 2011) this process convert inputs to outputs. Information produced has a purpose and to serve information need of some kind to meet a specific purpose or obligation. For example, a telephone directory created by taking the contact the details of a customer and recording them in a database. The contact detail are considered as data because they do not have any meaning when read, the process in order to be given a context, which is including the customer’s name and their contact details. This would enable the reader to understand this data as information because it has gained meaning. Processing data is a crucial step to make information (to place data into a meaningful context/information). There are multiple ways of data processing (transformation processes). Some appropriate examples of data processes are classification. This is when data gathered is categorised, e.g. in clothing a shirt could be categorised as white or black shirts. Amounts/numbers involves summarising data, calculating averages, totals or subtotals.’ (Bocij et al., 2008)Information eliminates uncertainty about events/situations so; therefore, it improves decision behaviour. (Bocij et al., 2008)

(Boddy et al.,2009) agrees with  (Chaffey and White, 2011)and (Bocij et al., 2008) on defining information and assert process of data being transformed into data can be done by people/Technology. (Dawson, 2015) adds that data which have been summarised and been given a context can be used as books, articles, recordings and even speech.

Converting data for rainfall into information can lead to graphs, which summarise monthly totals, seasonal fluctuations in different areas. In these formats, the data has a context/meaning now and the reader can understand what this meaningful data (information) represents.

(Dawson, 2015) states knowledge is describing our understanding of ‘why’. Knowledge is your higher level of understanding of things (Processed data to make information). Information provides concept of activities in the outside world, referring back to the word ‘why’ knowledge represents understanding. Knowledge is exclusive clarification of what you earn from information rules, patterns, decisions, models and also ideas. Understanding what was happening with the weather during rainfall over a period is information but knowledge represents to you why rainfalls have decreased or increased over a period since 2005 in a particular area. Very similar to this definition (Boddy et al., 2009) also adds that knowledge bargains for information obtained from data. Although data is a property of things (size, price, etc.) Knowledge is a property of people that inspires them to act in a particular way. Knowledge illustrates prior understanding, experience and learning, and is confirmed or modified as people receive new information. (Boddy et al., 2009) Knowledge can be reflection of connected results of person’s experiences and the information they acquire. (Bocij et al., 2008)Furthermore (Chaffey and White, 2011) asserts that knowledge is sequence of data and information with additions such as expert opinion, past experiences, skills gathered in the past which might benefit person/organisation to create an important asset in order to make decisions. This links in with (Bocij et al., 2008) because it acknowledges experiences are important, they play a big role to create knowledge. There are two types of knowledge tacit and explicit. Tacit knowledge is unstructured, difficult to codify not easy to explain or share to someone, expensive to transfer. Tacit knowledge is collective store of subjective or factual learning in an organisation. It includes organisations experiences, expertise, skills set and past/present experiences of staff.(Rainer and Cegielski, 2013) Example of tacit knowledge is the knowledge they need to drive a car. (Information and knowledge management, 2005) In contrast, Explicit knowledge is easy to share and impersonal and they are objective/modifiable. (Kayas, 2018: presentation) Explicit knowledge is unbiased, technical and logical.(Information and knowledge management, 2005) Driving manually/Highway Code is an example of explicit knowledge(Rainer and Cegielski, 2013) This knowledge can be recorded, includes procedures, reports which are conclusive and can be documented, then circulated to others.(Information and knowledge management, 2005) A business must have effective knowledge management skills; Knowledge management is section of process matured in an organisation to make, collect and accumulate and maintain the business knowledge.(Laudon and Laudon, 2018) Business classifies its knowledge within circulation of the knowledge; there are different sections within the businesses knowledge management, which needs to be involved with boosting the total effectiveness within organisation. (Kayas, 2018: presentation)

Tesco club card:

Dunnhumby founders arranged Tesco club card scheme, launched 1994 with 11 million operative holders. Customers fill in a form with their, personal information, address, name. Club card is used in tills while purchases are being scanned at tills (EPOS system) 10 million transactions weekly gathered are to analyse customers shopping patterns and identify them for example if they are a parent, cook, clothes lover, products refers to attributes for example cheap, expensive, traditional dishes, own label/upmarket brand. This information would include special offers (Coupons) based on customers buying patterns that is likely gain customers attention, annually Tesco mails these 4 times to customers which brings in a large increase for Tesco. This information on customers allows Tesco to find possible gaps in product ranges, promotional offers effects and nothing local variations. With paid information from Tesco when launching/promoting new products, brand managers can track who is buying their products or responding to promotions.(Boddy et al., 2009)

Data mining and manipulation for Tesco club card; Data mining is investigation of amount of data to find patterns which can help a business to make decisions and anticipate future behaviour. Data manipulation is simplified and more organised data, for example, lots of numbers can be gathered and this data can be manipulated by putting these numbers in order which makes organised and easier to understand.  (Laudon and Laudon, 2018)

Tesco club card uses data mining, like mentioned through club card they keep a record of products customers have bought, for example, if they are buying certain amount of drinks every week a pattern then can be seen, later on, this data will be manipulated by having sections of drinks sold within a week or different types of clothing sold in a week section. This data helps Tesco to make shopping experience fascinating for customers, due to the right products being on the shelves with the right price/promotions. Club card has the ability to show who purchases new products, if they are a current customer, shows repeated purchases. This can show if a new product such as a new cornflakes will be considered as a success or not. Tesco builds more on their customer profiles by relating to their consumption activities beyond supermarket shopping, for example a customer getting points from Auto centre is giving data that enables Tesco to gather data about the type of car the customer drives.(Rowley, 2005)

Data gathered with help of club card gives potential award such information/knowledge, not loyalty. Information gathered is weekly shopping of customer, success of this system can be measured by how many items are being sold, club card records sales very time its swiped at tills, quality of data needs to be considered, a single club card may be used by a family and this does not show which product each customer is loyal to.(Rowley, 2005)

Club card itself is a strategic activity carried out by Tesco, strategic level is about managers being concerned with long term plans, decisions maybe unstructured but usually has big impact on the business and it cannot be inverted easily. Tesco club card has a big impact on the business due to providing information on Tesco’s with customers weekly shopping patterns,  for example, this is enabling Tesco to come up with customised coupons so customers can buy their goods for a cheaper price, this will mean products will become cheaper in Tesco and eventually will beat rivals. This information is providing Tesco with how much of each product each customer is buying and how often it is purchased. Example being at black Friday they may find that many TVs being sold so during this day they would stock enough amount of TVs based on how much they sold last black Friday and club card showing the evidence with the buying patterns.

As an additional award this information can be analysed to become knowledge gained which is for example by after studying shopping patterns in a local Tesco store the right products will be stocked with customised coupons to buy those goods cheaper. Knowledge such as how much of each product to stock will become available, this knowledge is up to date and accurate which will mean that they will be able to sell the goods customers want and not what they might want for example promotions on a drink that is hardly sold within a Tesco store will not perform well in sales. This would predict the future sales for Tesco and would enable them to also ask their suppliers for the products they think that will sell well so suppliers make more of the goods that are more likely to sell than others.(‘The success of the Tesco Clubcard in winning customer loyalty’, 2004) With this vast amount of knowledge, Tesco will also predict what products they may need in the future and how much of it and they can cut down on buying products that will not do well, coupons sent to each club card member will match the products they put on shelves

In conclusion Tesco club card is doing a fine job of gathering data and getting useful knowledge efficiently. An improvement would be to identify why products are being purchased. Quick improvement can be use of panels with volunteers for market research and find out why people are really buying certain products, for example a customer may say branded goods are much more appealing and it provides a satisfied feeling.

References:

Bocij, P., Greasley, A. and Hickie, S. (2008) Business information systems: technology, development and management. 4th ed., Harlow: Financial Times Prentice Hall.

Boddy, D., Boonstra, A. and Kennedy, G. (2009) Managing information systems: strategy and organisation. 3rd ed., New York;Harlow, England;: Prentice Hall Financial Times.

Chaffey, D. and White, G. (2011) Business information management: improving performance using information systems. 2nd ed., New York: Financial Times/Prentice Hall.

Dawson, C. (2015) Projects in computing and information systems: a student’s guide. Third ed., Harlow: Pearson Education Limited.

Information and knowledge management. (2005)  Oxford: Elsevier Butterworth-Heinemann.

Jessup, L. M. and Valacich, J. S. (2007) Information systems today: managing the digital world. 3rd ed., Harlow: Prentice Hall.

Laudon, K. C. and Laudon, J. P. (2018) Management information systems: managing the digital firm. Global;Fifteenth; ed., Harlow, England: Pearson.

Rainer, R. K. and Turban, E. (2008) Introduction to information systems: enabling and transforming business. 2nd, International student version ed., Chichester;Hoboken, N.J;: Wiley.

Rainer, R. K. and Cegielski, C. G. (2013) Introduction to information systems. 4th, International student version ed., Singapore: Wiley.

Rowley, J. (2005) ‘Building brand webs: Customer relationship management through the Tesco Clubcard loyalty scheme.’ International Journal of Retail & Distribution Management, 33(3) pp. 194-206.

‘The success of the Tesco Clubcard in winning customer loyalty.’ (2004) International Journal of Retail & Distribution Management, 32(7)

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