Articles in Category : Retail Analytics

Maya makes use of machine learning, deep learning, advanced analytics, cloud computing, natural language processing (NLP) and generation (NLG), intent analysis and context-aware computing. But all you need to do is ask.

2. Re-interpreting Algorithmic Processing

Today’s digital business is generating millions of data points, across sources, every day. Taking traditional hierarchical approaches to analyze data is just not physically viable. Your solution should be able to conduct auto-discoveries, root-cause analyses, and auto-recommend best outcomes, based on simulations.

Manthan’s analytics platform algorithmically processes anomalies, outliers, and exceptions to recommend actions that can achieve clear, smart goals.

3. Re-interpreting Solution Engineering

As analytics processing becomes complex, analytics experience needs to become intuitive. Solutions need to be designed for the real decision makers and should embed real business contexts.

Manthan’s solutions are designed to bring together advanced analytics and algorithmic capabilities for specific use cases across retail. Our solutions also come with the ability to scale and to incorporate new use cases.

4. Re-interpreting Data Management

The digital business generates much more data than the traditional one, with new data sources emerging all the time. While some of this data drives repeatable use cases built around standard business processes, you also cannot lose sight of new use cases that elevate the customer experience.

Manthan offers a full-featured data management platform that can deliver production-grade enterprise analytics in a governed model. But at the same time, it also drives rapid experimentation and innovation with an architecture that can ingest and mash new data sources at scale in a data lake architecture. This supports on-demand data-processing, giving businesses real-time decision-making abilities.

We have what you need.

We have re-interpreted analytics delivery with AI. And an elastic cloud infrastructure and server-less computing capabilities provide the necessary performance and agility you need from a new age data a platform that can deliver real-time decision-making.

Tomorrow’s technology does not require a screen in front of you. Or you in front of a screen. It will walk with you, whispering real-time recommendations in your ear, based on intense, granular analysis.

That’s truly democratized AI. And that’s what we have for you, today.

A Tech Reboot: Why Are Fashion Companies Building Their In House Analytics Competencies?

Here’s why fashion businesses have moved from the traditional outsourcing model to building up their inhouse competencies:

Business Context: Unless you are the Kardashians, you understand your family better than an outsider. You know exactly what the business needs at a given time as opposed to a hired hand. When an activity is outsourced to an ‘expert’ outside the organization, they tend to make decisions without taking the business context into account. For example, a particular store might have lower sales because it had a new store manager and all the employees at that store are not aligned with his/ her objectives and practices. But an outsider looks through various forecasting tools and systems and has decided that a particular line of products, which would otherwise perform well, has to be discontinued.

Helps the business be more agile: Brand preference is no longer a thing. The choices are endless. And everything is needed here and now. But consider a business that has their decisions outsourced. Predictions for the season come in on Day 1. Orders are placed, and the production begins. When the products are displayed on Day 30, they realize that Cyan is in vogue and will be sold out by Day 35. A decision needs to be made on the spot by the store manager. But corporate has centralized operations to a team outside the country. Day 40, the store has completely sold out Cyan and is turning back customers. Day 75, Cyan is no longer in vogue, but the store is filled with Cyan pop-ups and unsold merchandise.

Quicker access to Data: The tech stack is complex, and data resides in multiple silos. The company has invested in so many tools and systems and has hired contractors from those providers as experts. To pull out a simple daily dashboard and present it in a format that is relevant to every store is a massive exercise. Even worse when every store manager wants the insights to be presented the way THEY want it. Compare this to an in-house team that has a system that unifies data from all the different systems and empowers business users to build their own dashboards, the way THEY want it, and you’ll see why outsourcing is not in fashion anymore.

Lower TCO over time: Fashion houses have long been hiring external experts, consultants, and hands to execute campaigns. But there is a problem. The problem of volumes. Executing multiple campaigns is turning out to be an expensive affair. As a shortcut, there are services offered by multiple consultancies and outsourcing companies that allow business to do ‘bare minimum’ marketing. Who bears the brunt of it? The customer. While agencies focus on each customer engagement as an activity, they tend to ignore terms like segmentation and personalization. Corporate CHQ might have contracted them to design fancy looking newsletters or trigger based SMS messages that go out in a set frequency and the customer is pounded with unrelated messages that result in churn at the end of the day. More and more fashion businesses are realizing this and are bearing the upfront cost of analytics software that helps them achieve personalization at scale. And they are now seeing the ROI with more customers engaging with the messaging and increasing spend with specific brands.

Bringing in a data-driven culture – In the world of Amazon, the fashion business is no longer just an art. A business that ignores the science part, is no longer relevant. Data-driven marketing has transformed from an innovative approach to a fundamental part of fashion marketing. Strategies are now built on insights pulled from the analysis of big data, collected through consumer interactions and engagements, to form predictions about future behaviors. This involves understanding the data you already have, the data you can get, and how to organize, analyze, and apply that data to better marketing efforts. Crafting experiences and engagements based on data has now moved to the boardroom and with access to real-time data and insights, even to the store.

For a more detailed view of what some of the leading fashion retailers are doing with their in-house analytics teams, visit our website

5 Steps to Mastering Customer Journey Focused Marketing in Retail

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 costs prohibitive with a long-drawn-out time to value.

Yet, they need a way to quickly connect these individual systems for an end-to-end view of the customer journey analytics. Customers today hunt down what they want at the price they want. They are open to sharing their data and being followed in their purchase journeys if they get value in return – whether it is in the form of better experience, elevated service, convenience, special offers or exclusive treatment.

For marketers, delivering on heightened customer expectations requires the use of customer journey analytics software at scale – acting on customer moments as they happen. Instead of reacting to customer-created journeys and go where they are going, you need the tools to manage, influence and even mold their routes to maximize their experience and your sales. Offering guidance, timely reminders (and perhaps a special offer) to customers who are wavering during the shopping process can yield significant business gains – reduced cart abandonment and improved conversions.

With more digital channels getting added to the mix, customer decision journeys are now maze-like.

We suggest five essential steps to set-up your customer journeys, without having to start-over:

  1. Collect all customer data – chances are you already have this in different systems. It is important to include all browsing and transactional data, irrespective of whether the journey resulted in a purchase
  2. Connect the existing data sources – Given the expansiveness of digital journeys and the variety and volumes of data, this isn’t likely to be easy but forms the foundation. This includes cleansing, organizing and resolving identity disputes. Given that retail business, today is as much about the seamless flow of information as it is about the flow of goods; it is important to have good data governance in place. Consider a Customer Data Platform that unifies all customer data.
  3. Switch on the data science – Start with connecting touchpoints during a single journey in real-time; to analyze customers’ deflection points, unusual behaviors, channel preferences and the kind of information they seek during their moments with your business. Some customers see more images, some are interested in product information, others read more reviews, while some other scout for offers. These elements are important to truly understand customer behavior and underlying reasons for jumping channels and device.
  4. Follow-up with the right messages. Now that you know where customers are in their journey and what they value, personalize interfaces such as email, website, mobile app. Customer insights, together with your expertise can help serve meaningful messages on the relevant channel that moves customers towards purchase decision or even do away with a likely hurdle as a special case (such as high shipping fee, few payment options etc.).
  5. Visualize journeys across the entire customer lifecycle. Connect a customer’s entire relationship to know whether she is just warming up to your brand, a repeat buyer, a loyal customer or at-risk of moving to your competition. This insight can be used to nurture the relationship over time and achieve upsell and cross-sell. Machine learning and predictive models iteratively improve journey performance and assess what individual journeys she is on at present, and what impact these have on each other and on the lifecycle.

In all of this, it is critical to continuously form and test hypotheses, measure outcomes and re-calibrate tactics for maximum impact. By automatically getting data-driven answers to questions such as ‘what kind of messages will further the customer’s journey’, ‘what is the best time to communicate with a customer to incite a favourable action’, and ‘which channel is likely to be effective for a wavering customer’, marketing analysts can maximize returns and optimize spend.


Today’s competitive retail environment requires a Journey Analytics tool that is built for scenarios specific to retail. It can enable marketers to create impactful customer-centric campaigns that target their path-to-purchase and measure journey targeting outcomes.

It is important that customer journey analytics tools automatically identify the best paths for each behavioral segment, with technology doing the heavy lifting of picking the most-suited channel combinations (basis both customer and fashion retail context) and best moments to communicate, with the end goal of accelerating conversion. The system should automatically be able to place customers in the precise lifecycle stage and move them towards the desired stage and outcome through micro-journeys along the way.

Getting these critical components spot-on and combining them with predictive insights can help you drive revenue. The results are bound to be superior to workbench-based tools, that only allow creating pre-defined paths and cannot react in real time to customer behaviors and actions.

The six steps to align marketing to customer journey in retail
The six steps to align marketing to customer journey in retail   Amit Rohatgi     April 24, 2019  Blog , customer engagement , customer experience , customer experience center , Customer Journey , Customer Life-cycle Marketing , Customer Marketing Plenty has been said about Journey Marketing and it is at the top of the list while evaluating marketing technology today. Rightly so. In a crowded retail landscape, customers have access to options and information from the comfort of their home, and just one below average touchpoint is enough to lo...


INTERVIEW: Grocery Retailers Poised to Reap Benefits of AI   Manthan Editorial Desk     January 14, 2019  groceries , Interview , NRF Randy Crimmins, EVP/Chief Strategy Officer at Relationshop (formerly GoThink!) shares his expectations with omni-channel marketing, retail technology innovations and AI- driven future of grocery chains...


Predictive Analytics – A Necessity for Retailers: Interview with Mindtree   Manthan Editorial Desk     January 11, 2019  Blog , Interview , NRF , Retail Analytics Vinaysheel Palat Global Head of Consulting for Retail, CPG and Manufacturing and Ronojoy Guha, a specialist in predictive analytics for Merchandising, Stores and Supply Chain logistics shares their top three tips that retailers need to focus on in 2019, to compete profitably. ...


Augmented Retail Analytics – Supporting Human Intelligence With Superhuman AI Capabilities   Ajith Nayar     January 2, 2019  Blog , Customer Centric Merchandising , customer experience , in store experience , in-store practices , in-store retail analytics , Merchandise Analytics , predictive analytics in retail , Real time Analytics , Retail Analytics , retail business intelligence , retail customer experience , retail intelligence , Store of the future The concept of augmented intelligence is not to replace humans, but to support human intelligence, meet their shortcomings, speed-up the repetitive processes, and enable them to take quicker and smarter decisions. ...


Automation and Augmentation of Retail Data and Analytics | NRF 2019   Ajith Nayar     December 12, 2018  Analytics trends , Blog , Customer Centric Retailing , IOT , predictive analytics in retail , Retail Analytics , retail business intelligence , retail customer experience , Retail data , retail intelligence As data explodes, the human ability to manually explore data, to find out issues or opportunities, and to device tactics to address them, is becoming a thing of the past. Read more to find out how a retailer can adapt to this change....


[Infographic] Why will you attend NRF 2019?   Manthan Editorial Desk     December 4, 2018  Blog , Customer Analytics , infographics , Retail Analytics , Supplier Collaboration , Target One , Vendor Link This year at the NRF Retail Big Show 2019, Manthan will be showcasing The Store That Knows. This offers analytics and insights 24x7, giving you smart recommendations while implementing your decisions. All in natural language....


Retail Prophet Doug Stephens Interview: The store of the future won’t be a “store”   Manthan Editorial Desk     November 12, 2018  Blog , Customer Centric Retailing , Interview , Retail Analytics The store of the future, in my opinion, won’t be a “store”. It will be a space that draws the shopper into a story about their brand and their products" says Doug Stephens, founder of Retail Prophet ...


Build or buy a Customer Data Platform? Here’s the answer   Varij Saurabh     October 29, 2018  Blog , Customer Analytics , Customer Data Platform , Omni channel retail , Omnichannel Retail , single customer view , Single View Of Customer Any marketer looking to invest in a CDP will have to grapple with the build vs. buy conundrum. Here we compare the build and buy options, with focus on time to value...


What goes into a Customer Data Platform Implementation   Rachna Manwani     October 29, 2018  Blog , Customer Analytics , Customer Data , Customer Data Platform , Customer Insights , Customer Segmentation , Micro-segmentation , Multichannel Marketing , Omni channel retail , Omnichannel Retail , Personalization , Retail data , Retention , single customer view , Single View Of Customer , Third Party Data Providers A successful CDP program requires cross-functional team effort. This implementation approach note will serve as a handy guide for B2C enterprises looking to ‘buy’ a CDP...


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