BI, a common acronym for Business Intelligence, refers to the process where information regarding the business, both locally and externally, is gathered, studied and used to make appropriate decisions for the company (Sangar et al. 2015). For a business, different kinds of dashboards can be used to identify and solve problems, but the common ones are the analytical dashboard and the BI dashboard.
The main differences between the two dashboards include: The BI operational dashboards are more useful for a user with a daily need while when a long-term analysis is required, then the analytical type is more preferred. For long-term use, more data usually gets needed which also tends to be more detailed than data for daily use. For day to day use of data, the information tends to be short-term and not too complicated; hence the operational BI is more suitable (Gardner et al., 2016)
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Also, the analytical dashboards are easy to understand therefore they offer a good chance for one to understand the background and history of the company. This is key in making future decisions for the company and also for making moves geared towards futuristic sustainability of the business. However, the operational BI dashboard is not complicated and it might not be the best for this situation. Because of its complex nature, it requires for thorough training of the procedures involved with it. Therefore, it is difficult to use it to implement plans for the business.
The analytical dashboards are also mostly used by data analytics for businesses because of their functionality and use. On the other hand, the business operational BI dashboard is used by regular business users because of their simplicity.
With the amount of data available with analytical data, it is possible to do in-depth research online as opposed to when using the operational BI. Also, it becomes easy to do research on the patterns of your business and pinpoint the available opportunities.
While the analytical dashboard is focused more on the past data to enable the concerned parties to make futuristic actions, the operational BI mainly deals with the day to day data collected from the performance and other such like indicators.
Here is what my approach to tracking the effect of a website coupon offer to be redeemed with an in-store purchase is. Since nowadays internet based coupons are frequent, it is relatively easy to track the activities of the voucher used by the client. I would make it possible for barcodes sent to an admin of my website for each purchase of the goods produced by the coupon. Since it is just an in-store purchase, barcodes are more suitable because the client will buy a variety of products from the same place. The barcodes can be translated to get the specific goods hence the activities of the customer can be tracked.
Also, for tracking of the customer through online shopping with the coupon, it is possible. Every item including those sold on the internet, have SKU codes which are used to identify the product. It can help to determine the outcome from the large pool of online database containing this information. It is also possible to track the websites that the user of the coupon visits and buys stuff from using the coupon by attaching a barcode to the token that provides the information.
Your approach to tracking the impact of a new online display advertisement for website purchases would be through engaging the customers during or after the ad. The analytical type is a more effective approach because it enables the researchers to know if the advert affected the customers. I would put click on videos in the ad for the clients. This is more effective to see the customer response of the website.
My approach to collecting and displaying near real-time shopper satisfaction survey results from shoppers’ mobile phones is by using internet inquiry grounded tools that rate the customer’s ratings. This method is fast and efficient, unlike the survey based questionnaires. The response from the research is also very genuine because the customers still have a fresh memory of the service rendered, so they do not have to recollect their memories to rate the service. Since this method comes in immediately after the service gets provided, the data given is real time.
I could use two ways to rate the degree of satisfaction. The commonly used star ratings and the poor to best degree multiple choices method. The method is very quick and efficient to the targeted customers because it is short and clear to the point. No unnecessary requirements of having to type comments about the experience which mostly seems cumbersome and tiresome to most people. All the customer is required to do is to check on the box the most suitable option.
Also, with the star rating method, it is equally as easy as the first approach because there is a variety of responses to pick. This method gets easily applied in all websites that can run on personal computers, tablets and mobile phones. The approach is more genuine than the survey-based method of collecting information which might not give accurate results for the customer. It is cheap and labor free too.
With the choices selected by the customers, data analytics is done to present the information in an excellent format to the researchers and the concerned parties and appropriate actions can be taken based on the data given. This also does not require labour because through proper programming on the back end, the data analytics takes place simultaneously as the rating gets done
References
Gardner, K. C., Broda, T., Cosby, K., Esau, D., & Hemmert, M. (2016). U.S. Patent No. 9,495,473 . Washington, DC: U.S. Patent and Trademark Office.
Sangar, A. B., Hesar, Z. E., Asl, M. S., & Tahmores, K. (2015). Research article proposing IS success models for measuring business intelligence system (BIS) success and analytical literature review on BIS measurement. ANARE Res. Notes , 33 (2), 269-283.
