Talk about how machine learning can predict and recommend based on various factors. In fashion, decisions such as color, size, fabric, design, style, preferred price range, where to promote are important. Similarly, in grocery, flavour, nutrition, convenience of use, dietary preferences, price point, pack-size, store location are critical attributes that a marketer needs to decipher.
According to the Harvard Business Review it can cost anywhere between five to 25 times more to acquire a new customer than to retain an existing customer.
Personalization is daunting as customers today interact with brands through a variety of channels both online and offline. The collection of customer data is scattered in legacy systems – with departments such as sales, marketing and customer service holding information in silos.
Gartner’s Hype Cycle for Retail Technologies 2018 is out, with trends for technology leaders. This year, the Hype Cycle has identified democratized AI as a key trend – products and solutions that “blur the lines between human and machine”. We are pleased that Manthan has found mention in 5 categories in the 2018 Hype Cycle. […]
Knowing the metrics that impact your churn can help you better understand why your customers are
disengaged. By identifying potential churners before they leave, retailers can take proactive steps to
This article looks at the various reasons why fashion businesses have moved from the traditional outsourcing model to building up their in-house competencies
Consumer facing businesses are crippled by every-growing touchpoints and siloed systems that don’t speak to each other. A complete overhaul of these legacy technologies isn’t an option – it is cost prohibitive with a long-drawn-out time to value.
This article explores 5 ways fashion businesses can use data and analytics to effectively increase traffic instore and online
“Manthan specializes in problems unique to large retailers”, “It does a good job of addressing the three distinct personas in the analytics value chain — business users, data scientists, and data engineers.” -The Forrester Wave™: Customer Analytics Solutions, Q2 2018