“Precisely,” means that you know the specifics of the niche services we offer and do not generalize your pitch for an entire industry.

We are not interested in generic or industry-wide software, applications, or services.

Consider saving the few seconds spent pasting your inquiry if you use the following terms without relative and specific context:

• “Transportation”
• “Logistics”
• “Supply Chain”
• “SEO”
• “Lead Gen”

We receive many inquiries regarding these “buzzwords,” and our system will ignore them. Consider this a fair warning.

The examples below serve as a rough guide for anyone choosing to sell us a product or service directly or through our contact page. If you choose to ignore these guidelines, our filters will likely delete your message before our server has a chance to receive it.

BAD EXAMPLE 1

Hi,
During this viral world wide outbreak each countries economies have been put on halt! This is the most important time to receive website visitors!

Why is your website – sfkcorp.com not featured on Google’s first page for most of your keywords?

You are missing about 50,000+ monthly impressions that could convert into visitors and future clients. Now that most people are at home constantly researching it’s the best time to indexed for high search volume keywords.

My name is FIRST NAME, I work for an SEO/Web Design company called COMPANY we are based out of NYC. After every country heals its people, the global economy will have a huge re-surgeance of consumers and you will want to be on top!

We help websites like yours with super low rankings receive proper rankings within 35 days or less!. I would like to send you a full website analytical SEO Audit ($250 VALUE) free of charge, I think it’s important you see the value we can bring you.

Please reply and let us know if you would like to see what we are all about visit us at WEBSITE our SEO packages are extremely affordable. Goodluck and godlbess you and your loved ones.

Best Regards,

Why it’s BAD: Everyone with a fair amount of internet knowledge can become an “SEO expert.” The real ones that shine (and those with whom we would consider working) are the ones who can Google their first and last name ONLY and be the first result. If this is not you, we can do our own SEO.

BAD EXAMPLE 2

We do custom software development and have executed sizable projects in Logistics & SCM domain. We are a CMMI level-3 and Microsoft Gold partner company with 810+ IT professionals . I would be in LA on 4th and 5th of April. In case you are looking for any IT solutions, I would be happy to come and meet you as per your convenient time.

Why it’s BAD:  Your credentials and awards don’t mean much to us if they do not highlight how, specifically, they will benefit us. “Logistics” and “SCM” are such broad terms in the industry, that by using them as a keyword, you have conclusively shown that you have not done your research on our company.

BAD EXAMPLE 3

COMPANY is full-stack software development company with extensive experience in Supply Chain and Warehousing domains. Recently we’ve finished the project, SaaS platform for our EU client, who provides Transportation services. Here are some achievements that we made:

•  Increased picking and shipping accuracy up to 95%.
• Reduced transportation costs up to 30% for inbound, outbound and inter-facility moves.
•  Optimized to 80% increases in transportation operational efficiency.
•  Improved Geolocation/tracking functionality.

COMPANY is a reliable partner for many clients over the world because of high-quality services with reasonable cost. Just let us a chance. With us, you’ll save time, money and nerve. Have a great day and looking forward to your reply soon.

Why it’s BAD: While the bullet points are excellent specifics, they also highlight how this seller knows nothing about our company. We do not ship products, and we do not transport cargo (our clients do). This seller is trying to sell us a service that we, very likely, are much better at than they are (we build custom applications for our clients.) Please do your research on us before you try to sell us anything “revolutionary” or “game-changing.”

GOOD EXAMPLE 1

Hello there, I am FIRST NAME with COMPANY, and we offer contracted O&G loss control services for clients who may want to explore business opportunities in REGION but do not have the capital to do so.

I have noticed that SFK works with global partners in regions it does not directly service, do you currently have any partners in REGION NAME of COUNTRY?

COMPANY has been a reliable expert in O&G commodities shipped from REGION and we regularly do contract work with regional surveying and consulting firms whose clients may need services outside of their operating regions. We use BIGNAME SOFTWARE for the general analyses and we have built several custom addons for it to cater to specific needs. We also built SELF APPLICATION for our clients to see activities and raw data in real-time. You can check out a demo at OURWEBSITE.COM/PAGE

I have a handful of example reports with sensitive information redacted. If you would like, I can forward you these reports to see how we can modify them to fit your company style and the specific needs of your clients.

Looking forward to hearing from you if our goals align.

Why it’s GOOD: This person did their research and specifically knew that we offer loss control services for O&G clients in our home region. More importantly, they understood that many of our clients in this industry need similar services at different global locations. They knew this because they are experts in one of our service categories, and they understand the dynamic and niche needs of this industry.

GOOD EXAMPLE 2

Greetings!

My name is FIRST NAME LAST NAME and I have been an agricultural QC specialist for over 20 years, working at various warehouses in the Seattle, WA area. After I retired in YEAR, I opened a small laboratory, LAB NAME, servicing the need for bio-analysis in Agri products.

For most my career, I had worked primarily with products from container vessels transporting reefer containers, specifically with Agri products from South America, so I know the common problems that occur with commodities from this region (PROBLEM1, PROBLEM2, etc.)

LAB NAME now services clients all over the US. We have an expansive network of shipping partners and can offer overnight analysis from most areas in the US. I know SFK Inc is in Southern California, our lab is in REGION, US WEST and FedEx can typically deliver samples the next morning from your area. We can have the results for you a few hours after we receive your sample.

I had noticed that COMPETITOR LAB is the closest one to you, so I am assuming you use them for a quick turnaround. I can offer competitive pricing to alleviate the added shipping costs and can guarantee results in a similar time frame.

If this is something SFK Inc. would be interested in pursuing, please do not hesitate to contact me, we can perform all the Agri related testing I had read on your Lab services page.

Why it’s GOOD: This person understood our needs, in a distinct category, to efficiently service Agri clients. They offered us the opportunity to vertically integrate services from our client’s point of view. They knew this because they learned that we work with partner labs and, as such, knew we were not always likely to capture the full scope of needs for each client. They also did their research on which of their competitors we were likely to use for the quickest turnaround. Finally, they knew the specific tests we needed and confirmed they could do them all.

A final note

Both of the “Good Examples” above have been from individuals with whom we now regularly do business. What made them stand out was their foresight for our (and our client’s) needs, and they were able to forge a lasting partnership by pitching a clear and specific business relationship.

We made this page because we have recently been receiving many more marketing inquiries for services and products we have no interest in using.

This page is not meant to snub you or your company. We are sure many of the products and services you are offering are a great fit for a company that would specifically benefit from using them.

We haven’t mentioned grammar or spelling in marketing inquiries, but they are important. We understand that English may not be the first language of many individuals, so trivial mistakes and minor errors are not an issue; we will recognize the talent or benefit if it exists. However, any inquiry full of conventional writing errors is a clear sign that we need to stay away.

Please use this page as a guide, and please try to understand the specifics our services offer, along with the needs of our clients. Any decent marketer should be able to glean our needs from the information on this site. We do not want to waste your time, nor you ours.

Click here to go back to our contact page.

Ichel, F., Pettitt, P., Shumway, L., Khan, S.

One of the most critical days of the year for retailers is traditionally known in the United States as “Black Friday.” Black Friday is the day after Thanksgiving and signals the beginning of the holiday shopping season. Because of the importance of this shopping day, most companies have sales with many items heavily discounted in order to draw consumers into its stores.

This analysis focuses on retailers Black Friday discounting strategy of new Apple iPhones. The players in focus are Apple’s distinctive Apple Stores, WalMart, and Best Buy. The three retailers act simultaneously to make corporate plans in advance. Apple will decide to discount or not to discount; WalMart and Best Buy take discounting as a given but must decide whether to discount high or low. Traditionally, about 65% of iPhones are sold through wireless carriers, the remaining 35% are split between Apple stores (14%), Best Buy (14%), and other mass retailers (7%), with Walmart accounting for half that number (3.5%).

In this case Walmart, Best Buy and Apple are each making a simultaneous decision about how they will price the iPhone on Black Friday. Apple Stores offer the best customer service and experience for consumers willing to pay premium prices and focus only on Apple’s products. Historically, Apple has been judicious with small discounts if any. The case for Best Buy and WalMart, however, is not whether to discount, but to what extent? Consumers are well informed as to where they can find the holiday’s hottest items for cheap, so the fight for all-important foot-traffic is related to who discounts deeper.

To establish that this game is played simultaneously, it is assumed that corporations must pre-plan their inventory, working capital, and advertising promotions before they can expect to hear what competitors have announced. The rationale is that by the time consumers start establishing their shopping plans for Black Friday, it is too late to change the marketing copy which will be prepared for distribution.

Each retailer has different strategies to thrive within particular constraints and opportunities, and subsequently, each will have a unique approach and different desired outcomes on Black Friday. Apple’s most desired outcome is that consumers view Apple iPhones as premium products, and are willing to pay a premium price for them. Second, Apple is concerned with maximizing its profits on Black Friday. Lastly, Apple would like customers to purchase iPhones in Apple stores because 1) higher margins, 2) increased sales of ancillary products, and 3) exposure to the in-store atmosphere, which leaves consumers with a desire to return. An unfortunate result of premium pricing is to limit the addressable market to high-income segments (even the iPhone 5c was priced aggressively at $550 in 2013, only $100 less than the superior iPhone 5s). Apple effectively uses retail channels like Walmart and BestBuy to price discriminate, or offer lower prices to a targeted consumer segment which is more price conscious, and therefore less likely to start shopping in an Apple Store. Customers walking into an Apple store are likely to be Apple fans excited to get their hands on premium products, and they are willing to pay a premium. But even these fans could be swayed to another retailer for a lower price. Also, these retailers attract massive foot-traffic which gives the iPhone increased opportunities to tempt customers, which is especially important during Black Friday, where the sale window puts constraints on consumers time. Since Apple likely gives retailers only 10%-12%¹ margin on each iPhone sold through those channels, most of the margin still accrues to Apple, so Apple’s main goal is to maximize sales by building a userbase of high-paying iPhone customers.

Walmart’s desired outcomes for Black Friday differ from those of Apple. Walmart stands out in consumer minds as a low-cost leader; thus when visiting Walmart, they expect the prices to be lower than anywhere else. The same expectations are maintained on Black Friday; I.e., consumers expect Walmart to have more significant discounts than anywhere else. Walmart’s primary goal for Black Friday is to get as many customers in the door as possible, and it does this by steeply discounting popular items. A loss-leader can pay-off by driving sales of high margin accessories and other goods.

Best Buy believes maximizing customer traffic would be ideal. However, it does not want to upset the supplier (Apple) by discounting too much, as they have less bargaining power. Additionally, a deeply discounted iPhone may cannibalize sales of higher margin phones for Best Buy. Consumers entering Best Buy already have it in mind to shop for electronics such as smartphones, where there are dozens of competitors, so there is significant cannibalization by the discounted product. Also, Best Buy could face pressure in the form of marketing dollars from Samsung and other competitors to promote their product more aggressively than the iPhone.

In this simultaneous play game, Apple is choosing between discounting the iPhone on Black Friday and offering no discount. It is not realistic that Apple would offer a high discount. Walmart and Best Buy are choosing between offering a low discount and a high discount on the iPhone for Black Friday. It is not realistic that either would not offer a discount and indeed they have done so in past years. The current dilemma then, is how the pricing strategy would affect the outcome of iPhone sales for each respective retailer as well as their respective goals detailed above. Given the situation at hand, the players must decide ahead of time what they will do on Black Friday, thus changing their mind last minute is not an option due to a high likelihood of sunk costs on preliminary promotion. Each player does not know what the other will do until a few days before the event, and by that point, it will be too late to change anything, thus making it a simultaneous play game.

Payoffs were determined by ranking each firms’ options from most to least desirable. The rankings and subsequent payoffs took into account each firm’s goals detailed above as well as how they would prioritize those goals. For example, Apple would prefer that customers purchase iPhones in Apple stores due to the higher margin and increased sales of ancillary products. However, that goal is less important than maintaining brand equity and premium perception. As such, the payoffs and rankings will reflect that Apple will prefer a strategy where a premium is maintained over one which maximizes sales of iPhones in their branded stores, all else being equal. Furthermore, the interplay between the goals was accounted for. So, while Apple wants to maximize Black Friday sales, there is a realization that maximizing Black Friday profits could result from higher supplier sales to Best Buy and Walmart as they discount the models, while not discounting at Apple stores in order to maintain brand equity, knowing that that strategy will result in low iPhone sales in Apple stores.

The game is set up in Game Plan as a three player simultaneous game where Apple either discounts or does not discount, and Best Buy and Walmart either highly discount the iPhone or discount at a lower rate. The setup, with all eight sets of payoffs, is shown below:

When the game is run, there are two Nash equilibria. In the first, Apple does not discount, Walmart discounts high, and Best Buy discounts low. In the second, Apple still does not discount, while both Walmart and Best Buy both discount high. The two solutions are shown below:

Both equilibrium strategies make sense from a business perspective. Apple never discounts, because the priority is maintaining brand equity, premium status, and can reach price discerning segments by supplying Best Buy and Walmart, who are motivated to discount. In the first solution, Walmart discounts high and Best Buy discounts low. This reflects the fact that Walmart makes its name off of low prices selling anything and everything, while Best Buy focuses on consumer electronics and sells competing smartphone brands and must consider cannibalization of higher margin phones when running promotions. In the second equilibrium, Best Buy also discounts high, matching Walmart, due to a need to drive foot-traffic and sales on Black Friday when a significant portion of consumer holiday money is spent. Best Buy is equally happy with discounting high or low and the payoffs are the same, though those payoffs reflect differing goals.

In reality, the past few years have seen the realization of this game as the retailers approach Black Friday. Apple never discounts the iPhone in its branded stores, while Walmart tends to run the deepest promotions and runs them dependably. Best Buy takes a stance in the middle, sometimes offering low discounts and other times offering a discount which matches Walmart’s. This game and the thought process through the payoffs gives insight into not only their business decisions but also their goals and what factors influence their payoffs. The key insight gained here is that Apple’s channel strategy allows for brilliant segmentation to meet consumer’s willingness to pay. This allows Apple to have it both ways – preserving the Apple store value in the mind of loyal consumers while offering a cheaper way to dip a toe into the stripped-down Apple experience.

In future analysis, it would be interesting to consider the implication of one retailers’ business model adapting to delay determining its strategy until after observing competitors move first. In such an extensive form game, the hypothesis to test is whether the last mover is better off than in the simultaneous game.

When we explore how changes in critical payoffs affect the outcome of the game in equilibrium, we note some peculiar findings. A sensitivity analysis focusing on two key assumptions could in fact change the game significantly for the players at hand. In our first assumption, Apple’s primary focus is on brand equity. If instead, we assume Apple’s priority is to increase sales through Apple Stores, we adjust our payoffs to favor outcomes; thus shifting the focus to sales volume, we see that Apple would, in fact, go for the discounting strategy. Particularly the most attractive outcome for Apple is to discount while other retailers are discounting low, to be competitive on price. The segmentation of the channels still benefits each company by reaching different customers.

Our second key assumption to question is margins for the retailers. While retailers normally receive a 10-12% margin on the iPhone, if that number was significantly lower, it would change whether mass retailers would continue to discount the iPhone high and take a loss. In our second analysis, we assumed very low margins on the iPhone (2-3%), and the results indicate that loss leaders such as Walmart would still be inclined to discount high, while Best Buy would always choose to discount low. The implications this has on the business case show that Walmart would still benefit from the increased foot-traffic, while Best Buy would not only lose money on sales of the iPhone but severely cut into the sales of it’s other higher margin phones. Under these priorities, discounting high would always be a bad decision for Best Buy.

A third assumption to tweak is Best Buy’s fear of cannibalization of other phones. If we relax this concern and instead assume high discounts will increase foot-traffic and increase ancillary purchases at Best Buy, then Best Buy’s motives would be more similar to that of WalMart.

Sensitivity Screenshots

In our initial simulation, we opted to procure only the more expensive items and order the other ones at the expedited rate, distributing them using a weighted average ratio based on the likely path of a hurricane. Later on, we opted to procure items based on the lowest total average cost, which was derived from 20 different scenarios. Additionally, our distribution methods changed to follow a variance of percentage model, where we opted to distribute supplies based on the lowest predicted likely cost of transportation. Our final recommendations revolve around The Home Depot opting more for our second model while forging relationships with local suppliers and distributors to reduce overall costs. Furthermore, we recommend The Home Depot stick to its comparative advantage regarding supplies and opt for the local suppliers to meet the demand of general supplies in the event of a crisis.

Core challenges:

As a significant home improvement and repair supplier, The Home Depot (THD) is a uniquely positioned actor before, during and after natural disasters such as hurricanes. THD’s retail and logistics locations and workforce are themselves affected by the disruptions caused by hurricanes, while consumers rely on the company’s products as they prepare for and respond to storm damage.  The challenges THD faces in managing these duals roles include the following:

• Predicting phases of customer demand during the hurricane season

THD faced difficulties in predicting the types and amounts of products demanded by consumers in storm situations. Ineffective forecasting efforts during Hurricane Andrew in 1992 resulted in both shortages of in-demand products as well as overages of products that went unsold and ultimately had to be salvaged.

• Predicting the hurricane path to reduce reallocation costs

Given the impossibility of predicting the final landfall point of a hurricane, THD is unable to definitively determine the best geographic distribution of products. THD must, therefore, develop a system to distribute goods in a manner that optimizes transportation costs with regard to all hurricane path probabilities.

• Establishing appropriate and ethical prices before and after the hurricane

In line with THD’s corporate values, the company established a “no-profit” policy during Hurricane Andrew to avoid price gouging during disaster situations. However, regardless of these price freezes, THD faces higher costs due to expedited pricing and transportation to supply its stores during hurricane situations. As such, proper forecasting is vital to ensure THD financial sustainability.

• Managing and maintaining workforce levels before and after the hurricane

THD prioritizes the safety of its workforce in the event of a hurricane. However, communications with staff during a disaster situation are difficult to maintain, and THD’s operations rely on workforce resilience and capacity to serve its market.

• Reputation and customer loyalty

Even as the operational realities of a company like THD become irregular during natural disasters, customers continue to build their perceptions of company reliability, product offerings, and customer service. Beyond THD’s direct revenue gain or loss during the storm situation itself, company reputation has a lasting effect on company success.

Lessons learned:

• Allocation

Initially, we attempted to manage costs by allocating resources based on the weighted average of the probable demand of all three scenarios. This seemed to be a sound choice, as when one considers the long-term effects of disaster management, a weighted average would lower overall costs in the long run. However, in our initial response, we stated that our allocation was based on a onetime event. Thus we wanted to allocate funding based on that event, and avoid any drastic fluctuations in price. That is, while the weighted average model would be ideal for multiple scenarios over time, we opted to reduce the volatility risk for a one-time event. However, after analyzing the shipment costs in addition to the expedited costs using the procurement model, we opted for a different allocation method, where we derived the numbers from a variance of percentage model (see appendix 1). Using this model, rather than calculating price based on a per item basis, we calculated the cost of moving one percent of the supplies between the regions relative to one another, e.g., the cost of moving 1% from region 3 to 1, or region 2 to 4. The percentage of variance calculations allowed us to better determine the high-cost regional shipment pairs and gives us a better conceptual understanding of where to allocate the supplies efficiently.

• Procurement Practices

At the beginning of the game, we worked on a procurement strategy that focused on delivering 75% probable demand for all the products most profitably by only ordering discounted items that are too expensive to procure expedited. We did this by optimizing the solution for a minimum value of combined discounted ordering and expedited ordering. Additionally, we opted not to order water bottles, as we determined the cost of expediting water during times of crisis would, in fact, be cheaper than the overall cost of moving the water between regions due to shortfalls.

Upon further analysis, we realized the problem with this approach: while it considers the combination of procuring costs involved, it doesn’t consider the costs of shortage and overage. The shortage and overage costs are important, as we are looking to reduce costs to the lowest 10th percentile, and these costs make up a considerable portion of the overall supply side costs. Another problem was assuming similar demand for all products; demand is more random and dependant on variables such as the extent of damage, power infrastructure, water, sanitation conditions, etc. So it is idyllic to consider various scenarios of demand for these products and model a solution which is optimal for any of these conditions and consider the overall supply costs, not just the procurement costs.

Recommendations:

As outlined through the lessons learned, there are a variety of ways to calculate the ‘optimal’ scenario. However, due to the sheer quantity of possible variations from the challenges outlined above, we opted to focus on a way to minimize overall costs by focusing on average cost increases, rather than try to mitigate the worst case scenario. One of the goals THD should focus on is its ability to provide those services which are its comparative advantage as a store. Thus regarding allocation, items such as bottled water should not be prioritized, considering they could be acquired from other retail stores.

On a macro level, another way THD can potentially mitigate some of its supply chain risks is by ensuring synergies exist between the command center and stores to promote coordination with the regional responses. Also, communication with other large-scale retail stores can be beneficial as there will be an industry-level need to improve logistics and can serve as a backup option in case there is any hold up with the current THD recovery plan. We recommend that THD continue its socially responsible, price stabilization policy to ensure it can provide to its customers in times of need. Finally, it is essential to ensure that their distribution centers have a more stable structure in place to be able to provide to the stores. In the future, THD can consider adding an additional distribution center, if it will significantly lower the expedited costs and provide more supplies when in need for a post-hurricane strategy.