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


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


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.


Decisions @Speed of Business


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


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.

Trends that will continue to rise in 2018 ( Part I)


“Change is the only constant thing” said Heraclitus – the philosopher from Ephesus who lived BC, 535-475. We already lived the first 2 months of the new year and we are curious about the advancements we will encounter in 2018 and as a player in the information technology space, I want to talk about what the industry should expect and how it will evolve.

In 2018 the trends of the previous year are expected to rise and converge. I will make my points in five headlines and this post will host the first two; Mobile and IoT.

Mobile: In 2018, mobile apps will continue to offer the customers a unique experience in the physical store and online, offering recognition, personalization, and interaction. In 2010, the share of mobile commerce in total digital commerce was around 2%, but today it is more than 20%. This increase will continue for sure, however, it is foreseen that around 2020, business models providing services on voice and AR will enhance the customer experience, make it easier & more natural and this may reduce the impact of mobile.

Considering that checking a mobile device frequently and looking at it for a considerable time brings some negative health issues, Voice and AR –  technologies that deliver experiences more suitable for the human nature –  will gain ground.

In 2018, mobile will play an effective role in sales conversion and payment. With more advanced barcode recognition standards and image recognition technology, products will be scanned easier and this will help increase the usage. Recently, Kroger announced that they will launch a new concept called ‘Scan, bag, go’ that will enable the customer to use their own device (or a device provided in the store) for checkout and payment, with no need for a cashier.  This was an extraordinary announcement since this was the first time that a checkout model with no cashier will be running in a considerable number of stores.

Store associates will use the mobile devices more effectively and efficiently while giving service to the customers. Especially for retail segments where customer service is crucial such as fashion or consumer electronics, the associate should have instant access to up-to-date information on the product and inventory, should be able to recommend other products, without leaving customer’s site. This will inevitably increase the impact of mobile in the store. Customer returns will be received with less effort by the staff using mobile devices.

 IoT: It is anticipated that in 2020, on a global scale, 34 billion connected devices will be on the market and $ 6T will be invested in IoT devices in the next 5 years. Which developments are supporting this rise? The decrease in sensor costs, the reduction in data processing costs, the reduction in bandwidth costs and the reduction in storage costs have great impacts on this growth. With these developments, the use of RFID will also increase. With this prevalence, retail customers will receive better service by accessing product reviews, high stock availability and digital coupons specifically used for the products with certain expiration dates, freshness and location. For in-house use, it will contribute to dynamic pricing and automatic ordering as well as real-time, up-to-date inventory and product information.

Combined with the blockchain, IoT exposed us to a new concept, BIoT.   BIoT – the combination of real-time and secure data – will emerge new business models. For example;  product traceability will be possible at last. Combined with the reliable environment provided by the blockchain technology, real-time data will make it easier to securely share business data across organizations, improving collaboration and efficiency, supporting collaborative business culture and customer experience.

I will continue the series in my next post and write on the impact of AI, blockchain and AR.

Highlights from NRF 2018


NRF 2018 took place between January 14th – 16th with the participation of retail industry stakeholders from all around the world, who want to keep the pulse of the future. I guess it would not be wrong to say the ‘Big Show’ turned out to be a ‘Tech Show’ as the focus in technology is increasing each year. I think the most important message of 2018 was that retailers who fall short to adopt technology – a real game changer – will have a hard time maintaining their position against the customer who is getting more digital and is changing the behaviors. The 2018 Technology Show was the exhibition on the effects of cognitive computing on customer experience. Although they have a broader portfolio of solutions, a significant number of information technology companies participated NRF with their cognitive computing, artificial intelligence and data-driven solutions, both in the exhibition and the conference sessions. There were 2 main missions that retailers and technology companies agreed on;

1. Customer expectations are augmenting day by day. We must further improve the Customer Experience.
2. We must use technology more effectively, not only for the customer but also to increase our business efficiency.

The prominent message of NRF 2018 for North America was the output of the Retail Economy Round Table. Gad Levanon, chief economist of the Conference Board North America division, predicted that consumer confidence would continue to rise and reach a record level. He stated that the 2017 holiday season had positive macroeconomic impacts on the 2017 economic figures. Retailers and vendors had consensus that 2018 will also have positive outcomes. Another critical issue was addressed in a panel chaired by David Marcotte from Kantar Consulting – Failures in international growth. The highlights were;

I. Problems in logistics and operations management, licensing and property management,
ii. Cultural adaptation; employee and product-related issues,
iii. Brand development; marketing, logo and naming can cause failure to grow in the international market,
iv. The payment methods, local parties in supply chain and logistics, compliance with law and taxation.

The most technology-focused scenario was based on a seamless customer experience with a highly efficient customer service; self-service and associate-free. Solutions like the components of an Amazon Go-style model retail in food retail were at the forefront. Fashion retail-focused solutions that detect customer’s body or foot measurements with 3D image sensors and recommend clothing or shoes based on this data, digital mirror or monitor used to order complimentary items for the one tried in the fitting room and systems that deliver customer comments on the selected item were the ones attracting most of the attention. I can express my takeaways from the event in 3 technology-nested headlines;

Restructuring of shopping processes:

We are witnessing that shopping starts with non-traditional methods. With kiosks running on augmented reality & hologram technology, smart shelves displaying information & 3D moving images on products and easier access to rich and real-time data, information technology has become part of our daily life. At the NRF, there were booths exhibiting vending machines – for consumer electronics, cosmetics and make-up retailers – which automatically recognize the customer and deliver an easy and secure shopping experience. It is possible to provide a better service to the customer without incurring square meter based costs. For example, Apple’s business chat application coming with iOS 11.3 in 2018 Q2, will enable the user to interact with the businesses without making a search on iMessage to find the business support accounts. The consumer will write a text and the message will be conveyed to the intended recipient; sales, customer services etc of the targeted company. Amazon Go, which started to serve the employees almost a year ago, was still an attraction point.

Focus on the importance of physical stores and sales staff:

Physical stores play a critical role in building relationships with the customers. 9 of the top 10 e-commerce companies have at least one physical store. However, physical stores are not utilized enough to further strengthen customer relationships. According to statistics, almost half of the Gen Y and Gen Z visit the physical stores, more than they did the previous year. The physical store is becoming much more critical to develop the brand’s relationship with the consumer, beyond shopping. This shows that a differentiated, more personal service and a better demand forecast will be more crucial. At this point, I have to mention the growing importance of the sales associates. The technological advancements serve the customers and increase customer satisfaction while they can also serve the staff and increase employee satisfaction. The sales associate applications – for the gusto-driven consumption retailers – that help the staff access the customer’s profile & purchase history, enable transfers of items across stores are powerful tools. On the other side, equally powerful tools for the grocery retailing are in-store applications helping the associate with shelf layout, inventory management – notifying critical conditions and enabling interaction of the staff. Thus, it is crucial to employ smart systems, mobile technologies and infrastructures both for building self-service customer experiences and for making real-time data available to the associates.

The importance of external factors in consumer demand, weather:

As in recent years at the NRF, the data-driven forecast and optimization had the leading roles. But the highlight was the automation of these smart systems. Another noteworthy concept was the importance of predicting demand based on external factors such as weather, local events and special days. Due to seasonal irregularities of our time, the weather seems to gain importance as a demand forecasting factor which highly affects the supply chain costs and planning. For example, the vending machines that are getting more popular day by day, utilize data such as the weather conditions of their exact location on the street and events/shows in the vicinity. Managing a business a vending machine efficiently is almost equivalent to anticipating the exact demand.

4 Ways Artificial Intelligence Will Penetrate in Retail


The use of AI technologies in the new generation enterprise and customer solutions seems inevitable. According to a research conducted by Gartner in 2016, AI was not among the top 100 technology areas to be invested. However, in 2017,  organizations and IT Managers made room on their agenda and AI ranked as the 7th technology investment priority. According to another recent Gartner study, AI will be built in most of the products that will be marketed by 2020. One of the top 5 areas where 30% of companies will invest will be AI-based solutions. Artificial Intelligence will also be a secret sauce, retailers will use as a game-changer, to support growth, to give customers a differentiated experience and to make a significant contribution to competitive advantage*.

AI will penetrate the processes with 4 main approaches;

  1. Minimize customer’s effort in shopping. From Augmented Reality to the rise of ChatBot, many of the emerging user experiences have a common point, accessibility and serviceability; anywhere, anytime. Today, we have come to the point where we can order coffee with a mobile assistant or ask a Facebook Messenger bot when we need an umbrella. The experimental store concept, which Amazon has just launched is a good example of effortless buying, empowered by AI.
  2. AI injected into the workforce. AI reaches every corner of the store’s back office processes and in-house operations, enhancing its effectiveness. Artificial Intelligence seems to help employees work faster, add value and be more accurate rather than stealing their jobs. The trainings will be delivered by AI, continuously and interactively. AR will help to service the customers with the products not present in the store or those not even produced yet. For example, Nike recently developed a store concept in NYC, called “Nike By You Studio” that allows the customers to design their shoes – with the help of a designer associate- and having them on their feet just after 90 minutes**. With this customer experience process characterized as “Nike Maker Experience”, the system uses AR,  object tracking and projection systems to show the selected design on the shoe.
  3. To take advantage of the opportunities provided by the platform economy. Business models like Ebay, have been working for decades on platform economy. Today’s platforms, integrated systems and IoT transmits the consumer’s requests directly to the manufacturer in real-time. Smart home “hubs” are offered over platforms like Samsung, Google, Apple and Amazon. For example, by pressing a button (Amazon), a user can place an order for a laundry detergent or diapers. If you like the song you listen to on your smart speaker, you can ask Microsoft Cortana to buy it for you.
  4. Focus on customer needs and redesign to facilitate the shopping process. Customer-related processes can be made more efficient and effective with modern engineering and design. Design-oriented engineering, also called “DesignOps”, works on processes that cause difficulties in customer interaction; such as problems getting product support or trying to find what they are looking for. Speech interfaces (chatbots), process automation, and AR can provide ways to deliver a better customer experience.

While we are already making room for AI in our lives, we have to be careful not to make the cure worse than the disease. Unexpected discomforts may come up by the use of AI or robots, in customer interaction or collection of personal data. It is critical to timely detect such inconveniences and to take quick actions, not to jeopardize the customer happiness.

Every new ‘digitalization’ and ‘intelligent automation evolution’ step will influence our lives significantly.  For this reason, businesses should be careful to act within the ethical framework while innovating and make sure they perform cross-tests to predict the effects of the new processes.

(*) Why retailers need to prepare for an AI- first world, today – and how they can. Avanade’s Tech Vision Report, 2017

(**) Nike’s new tech creates custom sneakers in under 2 hours, Stephanie Pandolph, Business Insider, September 2017.


A New Approach to Customer Engagement


Competition has always been a challenge, getting tougher every day. No secret that a business outstrips the rivals, only when the customers are satisfied and engaged. As Peter Drucker put beautifully; “The purpose of a business is to create a customer.”

To claim customer engagement, there should be customers, coming back over and over again, to purchase the products or services. It is critical to know the customer and predict the behavior, to take any possible action. We have been told that CRM is the approach and Loyalty Program is the tool, but that was some decades ago.

I witness that this perception didn’t give any ground since I still hear retail professionals say  “loyalty card is the way to collect the data and to know the customer”.

Oddly enough, the transaction logs conceal, so much more than the customers declare themselves, on a predesigned form.

I believe the reason behind the retailers’ assumption is, being stuck – for years – between RFM Segmentation with no reasonable action, campaigns sent out with bulk messaging and failure to personalize.

As technology advances and the processing power is more affordable,  we – technology providers – can easily incorporate mathematical models in our software and deliver you high-performance solutions. It is not a myth anymore; You can know your customer, better than him or her. With “Data-driven Behavioral Segmentation”, it is possible to develop a new approach to enhance customer engagement.

I would like to take this occasion to update you on what we have been working on lately, regarding this new approach.

  1. With no doubt, the online and offline channels should be integrated. The transactions made in the physical store and the online purchases should be aggregated on a customer record. Although there is progress compared to the early days of multi-channel, the pain point I get to hear the most is that the retailers cannot trace the customer who is ‘identified with an email address while shopping online’ and is ‘recognized with the phone number in the store’. In pursuit of the omnichannel, the retailers are having a hard time deduplicating the channel customers. Applying the Detailer Deduplication model, we helped retailers overcome this hurdle.
  2. We depict the ideal customer and the behavior set, extract all the alike-unlike sets and customers from an ocean of data. Certainly, this is a tough job, however using the Big Data approach, we are able to analyze with high performance and accuracy.
  3. Without the hassle of inputting the variables regarding the sales dynamics, marketing strategies, we work on the T-Logs to deliver the correlations. We incorporate data mining methods to extract the rule sets. We shed light on the probability of selling Y to customers who bought X, the lift factor of X to the sale of product Z.
  4. Predefined segment structures that encapsulate the customers in “not so accurate” groups are no longer needed. The new approach is to segment the clientele solely on behavior; monetary scale, the composition of the basket/categories or the timing of the purchase – weekday, weekend, morning, between 14:00-15:00.

Let me wrap it up!

Don’t lose time with the data collected at the first encounter, declared by the customer, filled in the boxes of a form designed by the retailer, which is no longer up-to-date. Take time to evaluate the Big Data & Data Mining solutions and bother only to collect the email and phone number from the customers, for communication & easier deduplication purposes. Your data has millions of stories to tell.

If this is on your agenda, let’s have a chat!

Listen to your data, make intelligent decisions.

7 Retail Customer Expectations Cognitive Computing Will Resolve in the Future


In a previous post- Cognitive computing and Customer Experience in Retail – I defined Cognitive computing as ‘the simulation of human intelligence & the thinking process;  thinking, reasoning, learning &  taking-action executed by the computer systems. In other words, it is an approach to perform the five human senses through information systems.’ We have been witnessing the critical changes in data-driven decision making and analytics systems & approaches, in the past years while creating solutions and initiating projects.

Let’s have a quick tour and understand how did we end up here?

In very early days, we started developing solutions for our customers to find the answer to their question “What Happened?”. After understanding this, the obvious question was “Why did it happen?” and we created solutions enabling interactive analysis, to find the answers. Any decent professional who knew the answers by then, started asking “What may happen?”, encouraging us to deliver our predictive solutions. Who can stop anyone to ask “What action should I take?” when they learn what is likely to happen in the future? Today the question evolved to the final – for now –  form. “What is the next best action?” and we are adopting cognitive computing approach, to deliver the answer.

According to the Louis Columbus research published in Forbes in March 2017, 70% of retailers are expected to run cognitive computing systems by 2021. Minimizing out of stocks with real-time inventory tracking and operation monitoring, providing better and more personalized services to the customers, will be embraced as ways of creating value.

With the solutions we offer, the retail industry has come to the point of making the right decisions automatically, with the real-time, operational and customer experience data they have in hand. The efforts to deliver a more personalized and high-quality service to the consumer, with less time spent on the consumer’s side, did not only increase the operational efficiency but improved the customer experience, as well.

We are now at the point where the consumers will start judging the industry norms, with simple but impressive styles.

A study published by Synchrony Financial in March 2017 reveals predictions about the future customer expectations. According to these predictions, we will soon meet the DIY consumer.

  1. Customers have already begun to gather more information from content providers such as, search engines and social media before they even come to the store. Some are more informed on a specific product than the sales associates, as they enter the store. In the future, the consumer will get the information via sensors on the products, pay with fingerprints or smartphones, without accessing any POS or waiting in the queue. The associate will be in the store as the expert, to be consulted.
  2. Fitting rooms with interactive mirrors will be the medium to call a different size, to put an order or even to pay.
  3. No need to wait for the store to open. There will be 24/7 sales. Even if the store is closed, orders will be placed at the storefront, delivery will be done by robots.
  4. Physical stores will be preferred for experiencing the product, entertainment, learning and getting help. For example, a 3D face modeling application, that perceives the face shape of a person will suggest a personalized model of eye-glasses. After the selection of one of the choices, 3D printers will be printing the final product, while the customer is in the store,
  5. Entertaining, having a good time, listening to a mini-concert or having a cup of coffee while shopping, attending a course to make artisan chocolate or an original leather belt in the store, reserving a fitting room; These are likely to be witnessed in the close future of retail.
  6. Customers will start asking “Why am I going to the store? Stores should come to me!”. With voice-driven systems, customers will expect the product or service to wherever they are, at home, in the car, etc, just by calling their name.
  7. Most important of all, the customers will be more time-conscious. They will expect to have what they want, right away. Customers will not settle for any delivery or return pickup that exceeds an hour.

As the expectation of the customer’s experience grows, we will look for an answer to the new question “How can we meet this expectation in  a better way?” Apparently; brands adopting systems based on cognitive computing will be differentiated in every field and will be leading the competition ahead.