Way too many among the managers of small & medium enterprises feel they are too small or they are not yet prepared to benefit from applied data science. Sometimes they are right and sometimes wrong. This is rising the question, when actually a business is ready to generate more profits/savings out of their existing data? How big it should be before consideration of data crunching. If a manager doesn’t dig deep enough into capabilities of applied data science or doesn’t have data analytics expert or at least consultant by their side, it is quite easy to miss significant opportunities for positive change and growth. This blog post is focused on this specific perception.
Let’s focus on businesses taxonomy regarding of their data maturity. They are five (relatively broad) levels of business data utilization as you can see from the graphic bellow.
The shopping spree of November and December is already over. Retailers worldwide are exhausted by all preceding processes and tasks up until the final execution. However, a big question will remain when we take a deep breath, prepare the reports, and close the Q.
Is there a way for retailers to make that high season easier?
The simple answer is yes and is delivered by automation. Retail automation is way more than a buzzword or a process mastered by Amazon and Walmart, playing in their league. The McKinsey experts say that automation and analytics have the dual benefit of boosting profitability while also freeing up resources to focus on the tasks that matter (such as new growth opportunities).
This blog post will focus on a few areas where applying automation can give retailers real value in their transformation journey.
Живеем във времена на драматични промени и ритейл секторът не прави изключение. Тук не става дума само за неизбежната технологична трансформация на този тип бизнес, но и за промените в потребитеските нагласи заради COVID-19. Много ритейл бизнеси не успяват да отговорят на предизвикателствата. От 2015 г. досега, общо 128 крупни ритейл марки са обявили фалит само в САЩ по данни на CB Insights. Сред тях са вериги магазини за храни и напитки, дрехи, обувки, мебели, спортни стоки, стоки за свободното време и т.н. Ето защо поставяме фокус върху наистина важните ритейл KPI показатели и разказваме как изкуственият интелект и автоматизацията на бизнес процесите помагат те да бъдат оцветени в зелено.
Retailers today must keep up with the ever-changing environment and customer expectations to stay competitive. Businesses are facing multiple challenges, including but not limited to volatile sales and fierce competition. One way to accomplish this is through retail analytics.
This is why data is essential for any retail business. The business numbers like average transaction value or online visitors should be monitored to gauge the performance effectively. What we have to have in mind is that numbers alone don’t tell the successful retail story. To gain meaningful insights affecting financial results directly, businesses are utilizing retail analytics.
Retail analytics provides insights on sales, inventory, pricing, trends, procurement, and many more variables that directly impact the decision-making process. One of the most important of them all is retail demand forecasting.
Artificial Intelligence /AI/ is all around us, and this is not a joke. It is literally everywhere. From chat-bots to smart assistants who are the most visible of its incarnations, through healthcare and architecture, AI has impacted all industries. Today AI is capable enough not only to help designing machinery much faster and more efficiently but also to create actual art.
Let’s face it – AI and its abilities are a significant part of our daily lifestyle, even though we might not suspect this at all. It is quite possible that your personal credit score is defined by AI. Just like the fruits you’ve just put in your basket – their logistic might be designed by AI. Preferences suggestions? Quite likely, they are products of AI. PC & console games, public administration, finance and pharmaceutical businesses, all of them rely on AI for one thing or another. Even the smart vacuum cleaners are advertised as the Artificial Intelligence that sweeps the floor.
“Only when the tide goes out do you discover who has been swimming naked.”
Those who act fast and adapt to turmoil will survive and succeed. In the context of the Coronavirus pandemic and the lending business, this means that financial institutions should have instruments to continuously analyze on a permanent basis what is happening and react immediately if needed.
As experts in credit scoring utilizing artificial intelligence (AI) and machine learning (ML), we have prepared this publication to offer an in-depth understanding of the problems the consumer finance business is currently facing. We focus on counteraction mechanisms, emphasizing the potential of automated decision-making in general. The dramatic change in people’s lives, the social distancing measures and the heterogeneous impact of the crisis on particular segments of the population, especially employment, as well as the need for additional financing led to a shock for the consumer finance business. As a result, some businesses have drastically reduced their operations and switched their credit scoring processes to manual loan approvals. This significantly reduced the speed and efficiency of their lending process as a whole. Some companies even chose to shut down part of their business abroad due to fears of significant deterioration of their loan portfolio and the negative effect of the imposed legal measures on debt repayment and collateral liquidity.
The COVID-19 pandemic is unprecedented in the recent days cataclysm. Countries from all over the world found themselves in a wholly new and, in most cases, unexpected situations. It applies to not just governments and businesses but also consumers.
The retail sector has been shaken by the dramatic change that forced some retailers to close their outlets, others to shift to e-commerce, while other businesses are facing drastic demand surge. When consumer behavior is rapidly changing, the right retailers’ response is essential, and their future might be at stake.
It is questionable if with January 1st, 2020 a new decade is starting but it is undoubtedly what are the hottest trends in AI & Data Analytics. Hristo Hadjitchonev, CEO of A4Everyone is sharing his own Top 6 for 2020. Just take a look and do not hesitate to use the comment form if you think he might have missed something interesting.
The students in the USA are eligible for school bus transportation if they live beyond a particular radius of the school. The yellow school buses are largely unchanged since their debut in 1939 even though their routes are getting more and more complex in the past 80 years. This is because of the number of students, schools, road systems and the system of rules.
Good retailers sell well, and best retailers offer their customers an unforgettable experience. The story of the 1800-Flowers.com online store is interesting because it traces the path that a Manhattan flower shop bought in the 1970s for $ 10,000 to a multimillion business that generated $ 1.2 bn. in sales just last year. The achievement goes through impressive AI innovation that has cemented its leadership position in the US online marketplace.