PLF During A Record-Setting Holiday Air Travel Season

More people than ever before will fly on U.S. airlines this Christmas holiday season – nearly 46 million of them – according to a new projection from the industry’s Washington trade group. With less than a month left in 2018 it now is almost certain that the average inflation-adjusted domestic round-trip air fare in America this year will be the lowest it has been in at least nine years. In order to accommodate the expected demand and to predict customer demand for specific flights on specific days, airlines should use advanced data analytics.

PLF (Passenger Load Factor) has always been an important metric for profitability which is highly effected by this holiday season. PLF measures the capacity utilization for airlines. It indicates the efficiency of the airline; filling seats and generating revenues. 80% of passenger load factor is considered as standard in the domestic airline industry.

PLF can be increased by improved forecasting of future demand or more appealing offerings. A high load factor, in itself, does not necessarily mean profitability. But in general, the lower the load factor, the lower the profit.

Considering the airline industry dynamics as cancellations of reservations, multi-leg flights complexity and the openings of new flight routes, forecasting PLF becomes even harder. Yet it is attainable by building airline-specific models to forecast the aggregate passenger traffic in a certain time frame, region or an individual flight. The model delivers an optimum revenue and enables the business units to cleverly act on price and campaigns.

The improvement of PLF is a direct impact on the bottom line. The bettering of this KPI effects the plan and costs of complimentary functions; such as workforce, fuel, catering and ground services.

Take a look at our PLF solution, which is developed on R and the visualizations are developed with MicroStrategy, to see how we can help you grow your business.

Don’t Cover Under Your Shelf(!) During the Christmas Shopping

There is no official start date for retailers to begin launching their Christmas holiday season sales. In fact, each year, different retailers set new standards to follow so the Christmas/holiday retail sales season is dynamic and continues to evolve. Retailers should be prepared for holiday shopping seasons.

On Shelf Availability(OSA) is one of the important metric for every retailer’s performance measurement during these seasons. OSA is defined as availability of product for sale to a shopper, in the right place he expects and at the time he wants to buy it. OSA is impacted by many different factors, all along the supply chain.

Collaborative Planning, Forecasting and Replenishment are mostly used collaboration efforts between retailers and manufacturers in order to reduce the out-of-stock rates. While there are some marginal improvements, the truth is that many of the problems are self-inflicted wounds. Retailers are not constantly putting the right products on the shelves. Key reasons for this are:

  • Store replenishment procedures fail to replenishing shelves on a timely basis
  • In-store inventory counts are inaccurate
  • Forecasting and assortment processes and systems do not adequately account for local demand
  • Space planning and planogram processes are not readily adaptable to local store footprints and demand

As a result, the consumer does one of four things: buy another brand at the same store; buy a completely different type of product; look for that brand in another store; or, simply, not buy anything. If the consumer does not (and cannot) buy your product because it is out of stock, you don’t sell. And, provide an opportunity for your competitors to gain your consumers business and loyalty.

There is a lot that retailers can do to get their own store in order for better on-shelf availability while they pursue collaborative supply chain efforts. The time is now! The complexities of delivering on consumers’ omni-channel demands for seamless and consistent shopping journeys will not go away, they will only become more complex. And as more retailers gear up to satisfy these demands, consumers are becoming even less tolerant of delivery failures. The time to transition planning and store operations to accommodate these increasing demands is now, or else you will be left behind as customers shift their loyalty to those retailers who best meet their expectations. For more information on improving on-shelf availability, check Obase Replenishment – Smart Inventory Management solution.

Black Friday & Cyber Monday Hangover

Four years ago, it was just a simple weekend. But now it starts a week ago and If done right, this final push can get your bottom-line goals across the finish line for the year.

Retailers are looking more towards online sales than brick-and-mortar. Holiday shoppers also flock to online marketplaces for Cyber Monday to find some of the best deals of the year in addition to Black Friday.

Sales and Marketing departments think that having the right product/campaign offers with highly competitive discounts is enough to get the most from BFCM (Black Friday & Cyber Monday). Of course it is a great achievement to attract audiences and get good sales results. However there is a missing part behind the curtains: Order fulfillment!

It is a nice but a big problem if you have made a record-breaking amount of sales and haven’t prepared for this! Here is what you should do:

  • Speed up fulfillment – Create pick lists, packing slips, and shipping labels in bulk.
  • Check with Partners – Ask suppliers if they might support you during this period with direct fulfillment to the customer from their own stock.
  • Be transparent – Informing your clients about expected delivery dates or any delays you will decrease any unsatisfaction.
  • Process returns quickly – By processing return items quickly, you can prevent customer churn and improve your corporate reputation and open place for other packages.
  • Be Reachable – Think email, online chat, searchable FAQ lists in order to ease customer to find you well. Think catching social media comments or praises to keep it under your control.
  • Recover your stock – When the rush is finished, recover your stock levels to continue on strong through the rest of the year.

Black Friday might be over but others are just around the corner. For the next special day use your data. Take a look back at your data from years past and pay special attention to marketing channels. Replicating previous campaign messaging with higher engagement rates can be key to driving returning visitors and converting leads with less budget.

Take the time to think through your strategy, what’s worked in the past, and what is going to be best for your brand long term. Do what you can to delight your customers, but in an effective way that still creates ROI.

Automation is key! Manually managing your inventory can be a hassle on any day of the year, but will be especially stressful on BFCM.

Take a deep breath and prepare for the next special day… Check out Obase solutions.

20+ years with Microstrategy

As one of the oldest Microstrategy partners our story has begun in 1997. When we first start, “The Intelligent Enterprise” term was very new to many companies in Turkey. That’s why it was a big success and very challenging for us to make Boyner-one of the biggest department store and Migros-the largest supermarket chain in Turkey to use Microstrategy products.

MicroStrategy combines traditional business intelligence with cutting-edge analytics, mobile, and cloud technology, enabling organizations to build and deploy transformational applications that maximize the value of information and accelerate business.

At Obase, as a partner we understand the power of partnerships and know the importance of collaboration to achieve success in today’s world. As a result of this by keeping strategic perspective at the heart of our partnership with Microstrategy, we were awarded as MicroStrategy Best Partner EMEA in 2009.

“The Very Firsts” in Turkey

During our 20+ years partnership with Microstrategy we pioneered many “firsts” in Turkey. We completed Turkey’s first Data Warehouse project. We prepared first Enterprise BI reporting and B2B reporting platform in our region.

Microstrategy Symposium Series in Istanbul at its second year became attracted by large enterprises’ decision makers and manager with 250+ attendees.

With a dedicated team full of Microstrategy certified engineers and also by being an R&D center we provide services and Microstrategy embedded enterprise analytics to the Top 10 companies in Turkey.

Today Obase provides license, maintanence and professional services with 1st level technical support for Microstrategy business intelligence platform to +50 leading companies from different industries; mainly in retail, telco, airlines & transportation, finance, insurance and energy.

Together with Microstrategy, with a highly futuristic vision and ability to follow the same path, at Obase, we will achieve to continue our partnership now and for the future.

We will meet you at Microstrategy World 2019 event.

Data-Driven Budgeting

NURAY GÖKMEN KAHVECİ | DIGITAL MARKETING MANAGER

It is that time of the year! 2019 budget planning is in full gear and you are trying to come up with new ways to make the right decision in order to create an efficient budget.

If you think it’s hard to develop a budget, you’re in a good company. Today’s motto is “do more with less”! Faced with high competition, companies need to spend every penny wisely. A Nielsen study found that %40 of respondents feel very confident in their team’s ability to determine the most effective way to allocate their marketing budget.

Today, companies often use outdated and complicated spreadsheets, older IT systems, and manually prepared reports. After spending long times to collecting and processing data, the lack of consolidated information and tools to perform analysis makes it difficult to track execution, program success, and convert data points into narratives that support their goals. This approach to budget formulation is labor intensive, less accurate, and harder to manage for budget purposes.

It’s obvious that the data is empowering new insights and business initiatives. Your data is out there and why not use it? Good judgement and good data are integral for leaders to make good decisions. By building multi-dimensional, structured databases, you can add new levels of transparency, accountability, and collaboration to some of the biggest factors impacting your budget planning and formulation.

Data-driven budgeting, also known as performance budgeting or results oriented budgeting, is more than a mere accounting of resources; rather, it drives decisions predicated on outcomes resulting from the use of resources. A performance budgeting process examines company/department goals and objectives, measures planning results, and uses this information to modify budget allocations over time. Successful planning replaces gut feel with objective, fast based analysis. This enables you to make better business decisions in support of company mission.

New Ways To Get Better Results:

  • Collect or share data in a way that is actionable
  • Input targets
  • Integrate budget data and enforce business rules and guidelines
  • Optimise your planning against the objectives
  • Leverage data to inform planning, programming, budgeting, and execution decisions
  • Monitor KPI and set an agile monitoring system

Instead of arguing for your budget, base your decisions and arguments on 100% facts. In order to make the chaotic world of budget and money allocation a bit more structured, facts based on data and conclusions derived from it are vital.

It is sure: data driven decision making is here to stay and budgets shouldn’t be based on crystal ball forecasts any more.

The Advantages of BDD

mıray tosun yurtseven | product manager

Nowadays, to prove the quality of a job, tests must be done immediately after the development. Tests are one of the most important evidences that prove a job is completed as desired. The other important evidence is the test carried out with business units. Business units test their usage habits according to the product they demand and their operations. Basically, they do their own behavioral tests. This approach brought a new test approach into our lives.

In these days, most people use BDD(Behavior Driven Development) which is an alternative method for TDD (Test Driven Development). Most people think that TDD is an expensive software development process and they prefer to use BDD.

The fact that the software development and test automation processes with TDD are considered to be costly, it shouldn’t be ignored that high quality software are developed with the TDD method. In today’s conditions, the necessity of completing customer demands as quickly as possible and in the meantime using a common language between Business Units and IT led to using BDD to solve communication problems.

In fact, as in all approaches, the main issue that the BDD focuses on is the production of quality code. In this context, in addition to solve communication problems between two different departments, BDD helps to find the bugs of a product very quickly with customer behavior tests.

There are two important workhops; first is SBE (specification by example), the second is TDD (test-driven development). In the first workshop (SBE), IT department representatives and Business unit representatives talk about a new product and its features, Business unit representatives give an example of where they want to use it and why they want to use it. The real purpose of this workshops is to decide how the system should behave in different situations . The second (TDD) workshop is carried out by runing the written automation codes to prove that the software has the desired behavioral functions. The test results are observed immediately right after runing codes, so that any inadequacy can be detected and turn into actions.Test automation for BDD has very simple writing method. Given-When-Then are the three titles of the test scenarios. Given is the heading of the test scenario to explain what customers want to do.  When is the timing detail of the action . Then is the explanation of the result of this action. For example, consider that we wrote the below steps for user to login to the system:

Given: User types a wrong password to the password field,

When: clicks the submit button,

Then: the system gives an alert message about the wrong password.

Another simple BDD writing method is Role-Feature-Reason matrix.   Role description begins with  “As a…”, for feature “I want…” and finally, “so that…” for the Reason. For example;

As a: retail customer,

I want: I want return the products I bought in 14 days.

So that: So that I will be able to payback.

The most important feature of the BDD is providing a simple platform where the codes are written in common language that lets everyone to read and write.. Due to its approach and creation of a common point for each stakeholders, it has become very popular recently and started to be used in many sectors.

Online Availability and NOLA Effects

BULENT DAL | CEO

Considering the fact that 30% of the store customers also shops online in the markets where digitalization is growing, it is important to consider the online availability, its effects and possible solutions. Recently published “Online Availability: A Worldwide Study of Extent, Shopper Reactions, and Implications for Non-Food Online Retail Categories” report indicates that the rate of not online available stock situations doubles out of stock rates in physical stores. The research, which is the most recent and comprehensive research on Retail online availability, was carried out on the basis of non-food products(baby care, home textile, personal care & beauty) within USA, China, France, Germany, Japan and UK.

Online availability; the fulfillment of orders from an e-commerce warehouse, a physical store or open marketplaces, if the product is available for online purchase. Out of Stock (OOS) used for physical store stock-out situations, for online it is called Not Online Available (NOLA). If the product detail page is visible but retailer states out-of-stock or the product is not displayed at the online platform (void); the products considered as Not Online Available.
According to the research, USA has with 15% rate NOLA which doubles  8.3% physical store OOS rate. This rate reaches up to 20% in non-USA countries and 8% of it occurs because of the void. There are two explanations of this situation: the first one is the product is not displayed due to stock availability, the second one is technical problems cause the product not to be displayed. Based on our experiences at Obase, we believe that most of the onsite search engines don’t support smart searches within the product lists according to meanings, categories and themes.

One of the most striking results of the research is that when the product is not available online the shopper’s reactions differentiate from the reaction to physical store out of stock situation.  While the retailer loses more in the physical store, at online this negative effect is reflected on the brand. Online shopper mostly chose to make another online search and buy a substitute product. According to the research conducted by the same team, based on shelf availability in the physical store, 45% of the total loss was reflected on the retailer. Shopper changes the store when the product is not available on the shelf or delays the purchase. According to online availability research results, 30% online shoppers switch to other e-commerce websites. The reactions to NOLA listed in 5 categories: 1. Switch e-commerce website. 2. Switch brand. 3. Switch substitute of the item. 4. Give up to purchase or delay. 5.Purchase from another channel(physical store,etc.).

According to Grocery Manufacturers Association (GMA) which is one of the supporters of the research, the potential sales loss of NOLA in a year is 17 billion USD. This research and report was prepared by the team that previously in 2002 and 2008 prepared the most comprehensive and referred reports in physical store out-of-stock(OOS) for GMA. Daniel Corsten, professor of business and technology at IE Business School and Thomas Gruen, a marketing professor at the New Hampshire University prepared the report.

Online availability has now become an important issue. In order to prevent NOLA, product master data consistency, real-time stock tracking, search engines supporting semantic content search, information systems based on consistency in demand forecasting and solutions are more critical issues to be focused on. As we always emphasize, the most critical solution requirement is the collaboration of a data-driven retailer and supplier.

Breaking News for Digitalized Customer: WalMart Jetblack, Digital Marketing, Face Recognition

BULENT DAL | CEO

The more the technology becomes a part of consumer’s life, the more it forces retailers to change their business models so that sometimes new technologies can be accepted without any questioning. After testing the success and usability, applications with the new technologies are disseminated with a few upgrades. Walmart tries countless business models based on new technologies in order to stay ahead of its major competitor: Amazon. Walmart realized CodeEight project under Jetblack  at the begining of June after a testing and trial period, which is a business model that offers personalized shopping services. Jenny Fleiss, CEO & Founder of Jetblack, explains that they passed out Jetblack with the “Consumers are looking for more effective ways of shopping for themselves and their families without sacrificing product quality” apporach. Jetblack operation that Walmart launched in New York allows consumers make orders with a plain text and get delivered at the same day. If it works, the business model will be live in all regions.

Another innovation from Walmart, similar to Amazon Echo model, it is possible to buy products from Google Home. Walmart recently, signed a strategic partnership with Microsoft to use Microsoft Azure infrastructure which is the biggest competitor to Amazon Web Services. In other words, the two of the Amazon’s biggest competitors in retail and cloud began to cooperate.

Digital  Marketing: Digital marketing is becoming more important than ever to connect with consumers those using internet connected devices. According to “Gartner 2018 CIO Agenda”, digital marketing activities like digital coupons, virtual storytelling, email, advertisements, etc. will have the highest share in new marketing channel investments. Again an example from Walmart, its digital marketing application which uses artificial intelligence that reminds consumer abondoned products in the basket when the consumer tends to leave the website might be beneficial to increase sales.

It is very common to find and order the same or similar product online when the color or size options are limited at the store. Consumer reviews on products are highly effective on buying decision. Being aware of consumer’s online shopping behaviour like searched products, interested products, abondoned baskets before they visit the physical store can help giving more personalised and related product offers via virtual sales assistant application.  Virtual reality helps improving customer experience. Sephora enables users to try on make-up products on their faces via its mobile application.

It seems that face recognition feature at smartphones like iPhoneX, will totally change the consumer shopping behaviour. According to Counterpoint Research Company, in following 2 years more than 1 billion phones will have advanced face recognition features which will increase the percentage of smartphones that have facial recognition features in 2020 from 5% in 2017 to 64%.

One of the examples among many retail formats that are beginning to benefit from face recognition is CaliBurger in California. The digital kiosk recognizes the loyalty card customers from their faces and creates a personalized shopping experience from their previous behaviour by giving similar product offers or listing favorite products. In another pilot application which will increase customer satisfaction, consumer’s instore activities and demographic details creates a micro segment, the real-time face recognition detects the consumer and creates offer according to the similar micro segment preferences and sends alert to sales assistant via an application to make the relevant offer immediately.

Face recognition technology also allows better understanding of consumer behaviours. For example, how much time spent to make the decision which cereal to buy, the emotional state of the customer at that time, age, gender etc. data can be used to create meaningful insights. These insight will lead to improve shelf layouts in store, real-time promotions, store specific processes.

Sources: https://techcrunch.com/2018/05/31/walmart-jetblack-personal-shopper/

Decisions @Speed of Business

BÜLENT DAL | CEO

These are times that companies need to show serious improvement in their decision making processes in turning data into decisions and actions. Even though in a world we talk about artificial intelligence and robots we have gone too fast creating automated decisions in digitalized processes, we still couldn’t catch this flow at companies with organizational hierarchies.

Most of the business decisions are made by humans as they are unstable and should be flexible according to the current situations in operational processes. When making these sort of data related to business decisions it is important to process the operational data as if it is done for raw petrol. At this point, as it is impossible to start a car engine with raw petrol, it is not possible to create meaningful actions for companies who don’t provide data insights for their decision makers.

The increase in data volume is higher than the data that turned into meaningful insights.  Within the past year, the companies which have data volume over 100 terabytes had doubled it. We are drowning in the data! However, the companies use only %20 structured data and only %10  unstructured data(social media, contacts, calls, video, sensors,etc.) in their decisions(*). Unfortunately, the % of used data will decrease, if we add filters to these decisions like smart, automated and optimum. The companies stuck themselves to a narrow window when using the data! “Drowning in the data, but not starving for insights” problem is caused by 3 main reasons: human, processes, and technology. The human-related reason is mainly caused by lack of teamwork between Business and IT teams when taking actions to increase data-driven business management process.  What has to be done in technology for data management is always the same for years. But due to working dynamics, it is impossible to realize it. As it is always the top priority for new store openings, campaigns, etc. for Business teams. When it is not a priority for Business teams to define an approach to their business management types, It is hard to expect big achievements with IT team’s solutions.

The most critical problem is the lack of the data-driven management approach from top to end decision makers in taking a business decision by looking into the right indicators for each process. When this approach is not adopted, the limited employees or data scientists will not contribute to the company. Even with the simplest BI tools, the results will be either subjective or unrelated to the real business processes.

%66 (*) of the companies keep their reporting and analysis data in excel. In other words, they don’t have a data-driven management approach! Decisions are taken either according to the personal expertise or thoughts on data sets gathered from manual BI tools and/or excel sheets. %34 of the companies get results of their decisions at the speed of business (**). For example, getting data to find an answer on why “A” product sells more than “B” or the profit decrease in “C” category will take with their entire tools and infrastructure. Only %3 of companies can create critical insights.

In order to increase data-driven management approach, it is a necessity to built a system to create calculated indicators and insights for decision makers other than letting them drowning in big data. Otherwise,  instead of having as many insights as we can get from our data, we will end up spending more time on analyzing data, calculations and getting insights, getting false results due to personal insights.

Sources: (*)Business Technographics Global Data and Analytics Survey, Forrester, June 2017.

(**) Augmented Analytics Is the Future of Data and Analytics, Gartner, July 2017.

2 Basic Requirements Of A Retailer Aiming To Reduce Inventory

BERNA BÜLBÜL | ÜRÜN YÖNETİCİSİ
MEHMET ALİ EKMİŞ | VERİ BİLİMCİ

The retail industry is undergoing a big change in terms of inventory. As you know, in many countries, inflation figures were very high in the 90’s, inevitably affecting the retail business. The most critical limitation in this space was – ironically – the limited space available. The economic landscape changed in the 2000’s.  These were the years when the executives figured out that piling up is not contributing to the bottom-line, on the contrary, it is a pretty bad idea. The concept of ‘Minimum stock level’ emerged and changed the inventory management approach. After almost 2 decades, supply chain professionals are still chasing the answer to a  question, where the bar is continually rising; “How can I make my minimum stock policy, even smarter?”. We will discuss in this and our upcoming posts, what a retailer needs to answer this question with confidence.

The 2 main prerequisites of a minimum inventory management approach  are;

1.Demand Forecasting System

It is no secret that, the success of an inventory management strategy is highly dependent on estimating the demand with the smallest error margin possible. While data flows from origin to the end, each step of the supply chain interacts with one another. Forecasting the demand is the first step of any production system and contributes to maintaining the stock levels, providing better service to customers, improving capacity utilization and bottom-line. Other than these, workforce, materials and capacity plannings are made based on forecasts. It is almost impossible to plan and manage in the absence of a solid forecast.

Retail demand is highly affected by discounts, campaigns and special day events, along with the seasonality and trend effects. To add another challenge, in retail it is common to see items removed from sale and then put back again, for certain business-related decisions. This fact distorts the time series and diminishes the confidence in the forecasts.

With a glance to retail, we see that some stores are located in city centers, some on university campuses, near stadiums, in the airports or on the coastline –  heavily affected by seasonal influences. A store in or near the university campus is directly affected by the academic calendar, as a store near the stadium is influenced by the league table. If the products determined to be affected by the special events or dates are not available in the stock room of the stores, customer demand will not be transformed into revenue. Since the effects are of different origins and nature, using a single method to compute the forecasts is not very realistic, after all. For this reason, we employ a multi-algorithm approach. We develop demand forecasting algorithms and race them to select the champion. We choose the best among the forecasting methods (exponential smoothing, regression, Holt-Winters, etc.) according to the nature of the demand and business.

2.Inventory Control System

The results derived from the demand forecasting system are input to the inventory control system. Thus every night, our system checks the constraints; customer service level, the rolling number to box quantity and stock on hand, decides whether the store needs to place an order on any specific product and if the answer is positive, computes the appropriate order quantity.

The value to carry less inventory grows each day and we believe that ignoring the data mining approach and maintaining the inventory management with human intervention hurts the business.

On our next post, we will detail the multi-algorithm structure in  demand forecasting.