SPARTA’s SP601 Data-Driven Research Fundamentals: ANALYTICS SOLUTION FOR DOOR-TO-DOOR SIM CARD DISTRIBUTION

CHAPTER 1 : INTRODUCTION

Background

The growing population of Philippines with regards to mobile phone usage as primary source of communication is a good sign of business for telecommunication companies but it comes with a massive competition not only with each other but also with the noise of other products.

Internet has pivoted many accustomed activities, and marketing is no exception. As more and more people are attached to their phones, companies develop a new era of advertising, the digital marketing. This study aims to solve the problems of marketing in the narrowing attention span of consumers and highlight the effectiveness of door-to-door marketing.

Smarta, a local telecommunications company, is considering to hire a marketing agency to do a door-to-door sampling caravan project to give out free prepaid SIM cards to households. The objective is to increase the subscriber base of the company while obtaining a net profit from this project by hoping that the free SIM card samples will eventually be activated in the future and revenue will flow from the activated SIMs.

Research Question

From the above background, the following question can be drawn the following main question.

- Is the door-to-door SIM card distribution effective to attract new customers?

Scope and Limitation

-The scope of this research is related to the business process proposal of sampling system given by the marketing agency to SMARTA.

-The study is limited to the population report published at the Philippine Statistics Authority website dated 2015.

CHAPTER 2 : LITERATURE REVIEW

Marketing and Its Growing Problem

Marketing refers to activities a company undertakes to promote the buying or selling of a product or service, (TWIN, 2020). Marketing evolves hand-in-hand with technology and thus produced digital marketing. But a problem arises on that, fierce competition on consumer attention (A new study in Nature Communications finds that our collective attention span is indeed narrowing, and that this effect occurs not only on social media but also across diverse domains including books, web searches, movie popularity, and more, (Technical University of Denmark, 2019)) and developing trust (Digital enables them to voice their concerns or dissatisfaction in an amplified way. In fact, we found that 40% of global consumers posted negative comments about an experience with a company via social media. Their digital distrust can easily influence thousands of potential customers to stay away (BENDER, BARTON, & MARQUE, 2017)).

Marketers Backtracked Towards Sampling

Marketers came up with different styles of product sampling and below are the two with direct access to household (University of Missouri, 2015):

Table 1: Product Sampling Method

Product Sampling Method

Product sampling continues to rank among the most effective tactics in the history of marketing because of its ability to do what no other medium can. Put the product directly in the customer’s hands.

Improved Door-to-Door Sampling

As above table portrays, available data and results are limited for marketers to implement the direct mail sampling. It also leaves a negative impact on potential clients due to the feeling of intrusiveness. Thus on reports of DMA UK Ltd (2014), door-to-door sampling is an extremely effective and impactful method of reaching and communicating with your target audience — which is why some 80% of the UK’s top advertisers use the medium.

Gap Analysis

Aim to further explore the relationships and effects in implementing the two marketing style (Door-to-door Sampling and Direct Mail Sampling) in the same adequate sample household.

CHAPTER 3: PROPOSED RESEARCH DESIGN

Research Design

This research applied quantitative and qualitative methods. The quantitative method was used to meet a reasonable cost for marketing with a time bound study of 100 days. Qualitative method used to explore the pertinent data to their conceptual framework that quantitative data could not facilitate (Crick, 2020).

Population and Sample

The door-to-door caravan used a non-probability quota sampling that was conducted around the City of Taguig and nearby city with a distance not more than 10-Kilometer and with approximate travel period that is not more than one hour.

Instruments

The data for this door-to-door caravan were collected by giving a standardized form for recipients to affix their signature to prove the acceptance of the SIM card.

Data Gathering

The instrument of this door-to-door caravan was administered and personally collected by the samplers. The SIM card distribution took 100 days.

PROPOSED DATA ANALYSIS METHOD

Quantitative

In cross-sectional design, we will use the descriptive and categorical analysis to illustrate the mean and deviation, percentage and frequency. In this study, prescriptive analysis will be utilized to determine the next decision to be taken by SMARTA.

Qualitative

On qualitative data, content analysis will be used to analyse the data from the door-to-door caravan marketing sampling conducted by 10 samplers.

CHAPTER 4: IMPLEMENTATION

Proposed Sampling System by Marketing Agency

The marketing agency proposed a Php5,000,000.00 contract (SIM card exclude SIM card costs) for SMARTA’s 100 day SIM card distribution. The marketing agency proposes the below sampling system.

Table 2: Marketing Agency Proposed Activity

Marketing Agency Proposed Activity

The above marketing agency’s proposed sampling system shows that the 10 samplers only have 6 hours per day to distribute SIM cards. Assuming that all the barangay that marketing agency will visit a population of 26,213 (average household size of 4 (Lungsod ng Makati, 2019)) that covers a lot area of 0.88km2 with approximately 5,698 of household like Barangay Bangkal of Makati (Lungsod ng Makati, 2018), samplers approximately 70.809 kilometers to cover per day.

Equation 1: Household Lot Length

Equation 2: Kilometers To Cover Per Barangay

In order to estimate the number of household that a sampler can visit in an hour, the marketing agency provided a 40km/hour as the velocity of the one sampler. Estimated of forty-five seconds spiel talk time (accepts/declines SIM card) and 30 seconds for household that do not answered the door was also provided by the marketing agency will total an average of 37.50 seconds. With the help Household Lot Length equation, the number of household per hour can be solved.

Equation 3: Number of Households Visited Per Minute

Equation 4: Walk Time Per Minute Per Household

Equation 5: Target Household Visits Per Hour

A target of sixty five (65) households visiting per hour with a six (6) working hours each for ten (10) samplers for one hundred days (100) days, there are 390,000 households could get in touch for the given sampling period.

Below chart is the expected hit and estimated conversion by the marketing agency:

Number of Expected Hit and Estimated Conversion

Exclusion of SIM card Php40.00 manufacture and Php50.00 credit with total cost of Php35,100,000.00, the marketing agency estimated an average return of revenue of Php13,162,600 yearly for SMARTA’s newly acquired customers.

SMARTA Implementation Plan

The objective of this SMARTA implementation plan is to increase the estimated new subscribers advised by the marketing agency. With the current situation, home-based learning was on effect to minimize and stop the spread of the virus. Unlike the marketing agency’s proposal, this plan targets consumers not only the close proximity barangays but students with demographics of 5 to 24 years that currently attending online school.

As one of the parents told (REUTERS, 2021) “Sometimes we change the SIM card to a different provider so he doesn’t have to study on the roof, but there’s rarely enough money to spare for that”. Consumers are now resulting to be more engaged on benefits offered by different telecommunication providers (Pew Research Center, 2019). Based on a study (Capistrano, 2013), the overall data shows that promotions and giveaways rated the highest level of involvement. This implies that generally speaking, mobile phone users exert more effort in learning about the promotions and giveaways being offered to them by the service providers more than the other service offerings. Thus, the SMARTA’s is on the right time to pursue door-to-door sampling caravan and target the students that are definitely at home.

The sampling will a benefit student and parents on budget and a contribution to the government for its campaign to promulgate online classes.

With the above socio-economic objective, the target barangays that has a 25% and above students on their population and the visit should not be per barangay per day but to visit the number households as reported from PSA (Philippine Statistics Authority, 2017). Below are the list of barangay and corresponding number of target students and days need to spend in a barangay given the rates from marketing agency.

The above barangays are projected to get in touch on a total of 502,190 students in 100 days. On such case, it is not needed to push outside of NCR for a door-to-door caravan.

If SMARTA pushes to reach 500,000 hits with a hit rate of 75% (answered the door) we will need 19 samplers to knock 740,741 doors in 100 days or 10 samplers conducting the door-to-door in 190 days.

Equation 6 : Household Visits

Equation 7: Household Visits Per Hour Per Sampler

Equation 8: Calculating How Many Samplers Needed for 100 Days

Equation 9: Calculate How Many Days Needed for 10 Samplers

Table 4: SMARTA Strategic Sampling with Cost

Strategic Sampling with Cost

Inaccuracy and incompleteness of sourced out data from Philippine Statistics Office is not enough to analyse it. This is one of the foreseeing reasons that may change the result of this study. Weather changes, samplers’ attitude towards the public, problems over the van’s travel time, which may lead to a bigger problem of under coverage that will take an effect on the feasibility of this project.

To avoid these problems and yet reach the target hit of 500,000, this study strongly recommend that the SMARTA or marketing agency will based on the number of acceptance target number rather than the per day in to pursue this door-to-door sampling.

Clearly, with a 53% ROI (marketing agency 46%) it will be cost efficient for SMARTA to take this study on their own. Sensitive variables will as mentioned above will be fully under control.

CHAPTER 5: BIBLIOGRAPHY

1. Technical University of Denmark. (2019, April 15). Abundance of information narrows our collective attention span. Retrieved from ScienceDaily: www.sciencedaily.com/releases/2019/04/190415081959.htm

2. TWIN, A. (2020, Aug 17). BUSINESS ESSENTIALS. Retrieved from Investopedia: https://www.investopedia.com/terms/m/marketing.asp

3. BENDER, M., BARTON, R., & MARQUE, F. (2017). Trust in the Digital Age .ACCENTURE. ACCENTURE.

4. University of Missouri. (2015, April 8). Course Hero. Retrieved from Course Hero: https://www.coursehero.com/file/p6o95um/Door-to-door-sampling-a-type-of-sampling-in-which-samples-are-brought-directly/

5. Frost & Sullivan. (2012). Research into the Door-to- Door Sales Industry in Australia. Frost & Sullivan for the Australian Competition and Consumer Commission (ACCC).

6. Lewis, J., & Ling, P. (2016). “Gone Are the Days of Mass-media Marketing Plans and Short Term Customer Relationships”: Tobacco Industry Direct Mail and Database Marketing Strategies. Tobacco Control.

7. Morimoto, M., & Chang, S. (2013, Jul 01). Consumers’ Attitudes Toward Unsolicited Commercial E-mail and Postal Direct Mail Marketing Methods. Journal of Interactive Advertising .

8. DMA UK Ltd. (2014). Door drop guide.

9. Crick, J. M. (2020, March). Qualitative research in marketing: what can academics do better? Journal of Strategic Marketing .

10. Lungsod ng Makati. (2018). Barangays. Retrieved January 2021, from Makati Portal: https://www.makati.gov.ph/assets/uploads/staticmenu/docs/1579227221918.pdf

11. Lungsod ng Makati. (2019). Facts and Figures. Retrieved January 2021, from Makati Portal: https://www.makati.gov.ph/cms/content/facts%20and%20figures/3241

12. REUTERS. (2021, Jan 7). ASIA. Retrieved from AsiaONe: https://www.asiaone.com/asia/scaling-roofs-and-mountains-philippine-students-battle-take-online-classes

13. Pew Research Center. (2019, November 20). Internet and Technology. Retrieved from Pew Research Center: https://www.pewresearch.org/internet/2019/11/20/mobile-owners-what-they-struggle-to-do/

14. Capistrano, E. (2013). Investigating Product Involvement Issues for Mobile Phone Services: A Study of Filipino Mobile Phone Users. Philippine Management Review 2013 .

15. Philippine Statistics Authority. (2017, June 30). Philippine Population Surpassed the 100 Million Mark (Results from the 2015 Census of Population). Retrieved from Philippine Statistics Authority: https://psa.gov.ph/population-and-housing/statistical-tables

16. Marikina City. (n.d.). The Shoe Capital of the Philippines. Retrieved from Marikina City: https://www.marikina.gov.ph/Our-City.html

Evolution of Logistics Analyst

Antoine-Henri, Baron Jomini a French officer defined logistics (1838; Summary of the Art of War, 1868) as “the practical art of moving armies,” The word clearly describes the movement of services of people and supplies to provide requirements in winning a war. Now, the word logistics is widely use by companies in defining their process of managing and planning goods and resources to serve its purpose.

Evolution of Logistics Analyst by Wilma Lapuz

The Progressing Role in Logistics:

1. Logistics Customer Service — this first line of defence in logistics. They are the one handling calls and emails. This role not only supports import/export/local department but also ad hoc jobs required by accounts or sales. They are the ones also responsible in general filing or other administrative duties.

2. Logistics Officer — this position ensures to support international/local order fulfilment and deliveries in a timely and efficiently manner. Sounds easy? Think again. They are the ones responsible in stock inventory and data entry, sometimes not only their own companies but some of the clients too.

3.Logistics Analyst — due to automation and rapid change of technology, you may notice some of the online job postings of companies are combining the two above roles and adding up analytics expertise on it.

Company Benefits Having a Logistics Analyst
Data is the New GOLD. In that case, logistics companies even freight forwarding businesses are seating unknowingly in a pot of gold.

So how logistics or freight forwarding companies can take advantage and stay afloat in the Fourth Industrial Revolution? If you will observe companies like DHL or Bollore they are actively hiring analysts now. Why? Because companies do not want to run their companies through gut-feel or crystal ball. Data companies like Google proved that many business will be more productive if decisions will be made using data.

Imagine, using the historical data of your clients you can anticipate the volume of materials they needed, from which supplier and what mode of transport. You can provide them quotations even if they are not asking. Wink-wink! That’s a customised customer service.

What else? Do you know why you keep on losing in bidding to get the project? Do you remember why are you winning? In aggregating your data you can anticipate the moves of your competitors.

Remember building relationship with your client and suddenly no more business calls from them? As a salesperson, will you meet them for lunch just to know what went wrong? Well, have you taken care of the KPIs of their stakeholder and provided customer experience? Remember customer experience now is valuable as getting your service on a lower price.

Of course, logistics analysts monitors not only the KPIs but also the procedures and practices to identify developments in planning and execution.

Do you need a Logistics Analyst even you are a Small/Local Company?
I want you to answer this question, are you competing with multinational company by giving quotations to your perspective importer/exporter? How can you compete if they their logistics analysts research the expectations of the clients in a certain project? Remember, some companies are willing to pay the price as long as you can meet their deadlines and KPIs.

Another thing to think of.. do you know that many start-ups that’s armed with data are now entering the logistics industry? They are providing quotes more quickly and offers an agile bid on clients. You know that in logistics/freight forwarding time is of the essence.

The line of difference in Logistics/Freight Forwarding industry is getting thinner and thinner. Competition is evolving fundamentally and companies should always examine whether they have the capacity and capabilities to change and compete.

Will you emerge and develop solutions to move forward and upward or you will just stay where you are because.. well I know, it is tough to change.

UC Davis: SQL for Data Science - Profiling and Analyzing the Yelp Dataset

Whats is Yelp? It aims to provide a one-stop platform for local businesses. Like other social media, it creates community. And that community discovers, transacts, and reviews local businesses around them.

For this post we will use Yelp dataset and will formulate ideas for analysis.

First, we need to explore the dataset. Below is the structure data in table form.

The Yelp Dataset

And for me to understand the above data and the entirety of its contents, I have visited the YELP website and marked the table names and column names against its pages.

The Yelp Dataset - Business

The Yelp Dataset - Review

The Yelp Dataset - User Page

Armed with understanding, profiling and establishing relationship among multiple tables, you can now look onto subjects that can be useful for different stakeholders.

Some of the business questions lingered on my mind upon seeing the above data were:

• What cities could be the best choice in setting up a business?
• Are there weight on the reviews coming from elite year users?
• What are the main words used in text reviews for sentiment analysis?
• Finding the effect of review_count and check-in on the life of a business.

I focused on the latter due to the limitations of SQLite.

SELECT business.name
, business.stars
, business.review_count
, business.is_open
, SUM(checkin.count)
FROM business INNER JOIN checkin ON business.id =checkin.business_id
GROUP BY business.name
ORDER BY SUM(checkin.count) DESC

Two (2) tables were needed to get the answer for the above analysis. I used INNER JOIN to avoid getting the ‘None’ value.

The below selected data will prove that even a company received only 1 star it is still open for business. That businesses that received highest checkin count do not necessarily have 5 stars and so on. Thus, this data means that there is no direct correlation on stars, review_count and checkin.

It would be good for YELP to indicate the current population for the neighbourhood on that business.

The Yelp Dataset — Correlation of Review Count, Checkin, and Stars

That’s all for now! Be curious y’all! Don’t forget to click the clap button! *wink-wink*