Articles in Category : Merchandise Analytics

MANTHAN: We hear experience becoming more important than the product – is that only for high involvement categories, while the drivers in other categories might be different?

DOUG: The consumer world is now divided into two very distinctly different and viable experiential spaces.  On the one hand some brands are winning through high-fidelity experiences that are immersive, memorable and emotionally connected. Other brands are killing it in their categories with high utility experiences that are frictionless, fast, convenient and very cognitively connected – they just make sense.  Both of these positions work and both are valued by consumers. The problem is, most retailers are neither high-fidelity nor high-utility and increasingly, that makes them irrelevant.

MANTHAN: Most businesses are struggling to become truly omni-channel – what’s your advice to them?

DOUG: I suppose I have two thoughts.  First, if you’re still working on omni-channel, consider that Amazon, Google and others are already dealing in the realm of omni-presence, in the sense that they’ve launched technologies like Amazon’s Echo that are quietly infiltrating the majority of homes in North America and available to consumers 24/7. Secondly, I’m not a big fan of the word omni-channel.  I prefer to think in terms of the customer journey with a brand and the various problems or opportunities along that journey.

If we can develop an intense and granular understanding of the consumer journey we can leverage the unique attributes of each channel to create the best possible solutions for the customer.  With this insight, we can then begin to build the back and front end systems and technology architecture to bring the experience to life! 

MANTHAN: Retailers that can best harness customer data will win – how far out do you see this becoming a reality?

DOUG: 200 years ago the local merchant that knew their customers most intimately won.  And they gathered information about their customers by being discreet about their privacy, delivering personalized recommendations and experiences and by building trust.  

Today is absolutely no different.  So, yes, retailers with the best data have an advantage. But getting that data means that a clear exchange of value has to transpire.  Data and privacy are no different than any other currency and consumers will spend their data with those brands and retailers that respect it and deliver clear value in exchange for it.

“I believe we’ll see a reengineering of the economic model for retail.”

MANTHAN: What’ does the ‘store of the future’ or ‘the intelligent store’ look like to you?

DOUG: 99 percent of the retail we see around us today is a relic of the 20th century.  It’s retail that was built for a pre-digital era and a completely different consumer reality.

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.  It will be less about the products themselves and more about productions – experiences that are interactive, immersive and fun. Technology will allow us to activate store experiences that are unique, personalized and adaptive based on unique customer preferences and needs.

Technologies built into the skeleton of the space will deliver real-time, website-like insights that will allow retailers to respond in real time to different customer groups and dynamics within the space.

Essentially, retail stores will transform from being the mini-warehouses they are today, to becoming entertainment and hospitality spaces that trade on social, physical and emotionally connected experiences.  Products will come along in the jet stream across channels. 

I also believe we’ll see a reengineering of the economic model for retail. Look for more retailers working directly with brands to create experiences  – what I call physical media –  for which they’re paid upfront, rather than being dependent on product sales.  

MANTHAN: Thank you, Doug!

Build or buy a Customer Data Platform? Here’s the answer

A packaged, industry focused CDP provides ‘people ready data’ and quick time to value

This article is part of our CDP series. To read other articles in this series, click here.

Any marketer looking to invest in a Customer Data Platform (CDP) will have to grapple with the build vs. buy conundrum. The points of view are more entrenched in this space than any other. This is primarily because the end deliverable is a customized data platform, which resembles a services engagement. Let’s compare the build and buy options.

If you know exactly what you need, can describe it in exact requirements and have a service partner or an in-house team who can deliver to the requirements, build might be a path to consider. You will still have to wait for 6 to 12 months to get access to the solution. If you have a stop gap technology that meets your requirements while you build, or time to market is not critical consideration, you can opt for the build route.

The build option

Organizations with substantial IT budgets and large development teams with a complete range of data, design and IT skills might opt for building a CDP in-house. The people investments required to deliver a custom CDP and maintain it are considerably higher than the buy option.

Done right, there could be competitive advantages with a custom solution. However, be cognizant that at every stage there are very real challenges – scope creeps, failure to accurately define specs, cost overruns, overheads of vendor management and staff attrition.

A large-scale IT project is often lost in translation – what the customer explains is different from what the project manager understands. Further translations happen to the engineer, and then to programmer, and alas, the result ends up looking significantly different from what was envisioned.

The buy option

A pre-packaged CDP that is crafted for a specific industry offers the best of both worlds – quick time to value of a plug and play solution and close fit of a bespoke solution. While you would need some technical resources for the set-up and upkeep, the time and costs involved are much lower, and there are no harsh surprises.

This means you can run pilots and POCs quickly before you go all-in, you can examine and are sure that the solution really works for you. A much lower upfront cost and quick onboarding takes away risks, and still provides a solution that is tailormade for your needs.

CDP that delivers people ready data

A well-designed solution must cover scenarios and requirements that haven’t been thought of and encountered before while being cost-effective.

A CDP should serve multiple user groups that have unique needs. It should be scalable to serve data scientists who work with large data sets and require clean, well-organized data. It should be flexible to serve marketers and campaign planners who want to promptly analyze metrics and decide targeting strategies, or perform customer segmentation. Senior executives form another user group, and would typically be interested in trend reports and business dashboards.

Recognizing the distinct personas in the value chain, a differentiated CDP must have distinct interfaces and functionality for each. It’s time you addressed the unique data needs of different people.

Download CDP Handbook Now!

What goes into a Customer Data Platform Implementation

Milestones to track, success criteria and key stakeholders

This article is part of our CDP series. To read other articles in this series, click here.

Customer Data Platforms are in fashion, but not a trend that will fade away soon. As a pre-packaged, marketer managed system that unifies a company’s customer data to enable use cases such as personalization and contextual marketing, a CDP delivers high value to B2C businesses.

A successful CDP program requires cross-functional team effort where marketing plays a critical role in defining the program objectives and success criteria. Given the nascent stages of the technology, there is little guidance available to businesses investing in a CDP.

This implementation approach note will serve as a handy guide for B2C enterprises looking to ‘buy’ (and not build) a CDP. Read this for our view on build vs. buy.

Broadly, a CDP implementation program has three phases:

Phase 1:  Business and Data Discovery

This phase involves understanding business needs, customer data and the technology landscape – along with buy-in from key stakeholders on how the unified customer record will be created and maintained in the system.

Typical activities include:

    1. Planning and requirement setting

Marketing (CRM/ loyalty and marketing operations) leads this phase where the ask is to define four key program requirements

        1. Marketing objectives – this could be improving loyalty engagement, customer retention , customer acquisition or customer engagement across touchpoints
        2. Challenges with current data – is it gaps in the data, poor quality or lack of trust in available data, or a combination of all
        3. Success criteria – metrics that business will track to validate the quality of customer data against marketing objectives
        4. Marketing campaigns – the types of campaigns that will be executed once transformed customer data is available, and how the data will be fed to marketing systems such as campaign execution tools
    1. Identify data sources

This session is primarily led by data and IT engineering teams who manage customer databases. Marketing technology and analysts may also be involved if they capture customer data via surveys or campaigns. Other aspects covered in this phase require understanding the primary source for customer master, data collection, integration and verification process

    1. Establish business rules for customer data unification

Based on available data and customer marketing priorities, CDP vendors work with marketing and data/ engineering to establish rules for processing and creating a comprehensive view of customer. Some of these rules could be around:

      1. Standardization and verification of captured customer data
      2. Prioritization of data sources for identifying customer attributes, for example, in cases where more than one value/ inconsistent data is present for a given attribute (such as more than one home store for a customer or multiple phone numbers for a customer)
      3. Establishing mechanism for de-duplicating customer records. This would require agreement on approach to run deterministic and probabilistic matches to create the golden record of a customer
    1. Customer data enrichment

This consists of not only adding customer data from third party sources but also enriching customer data with analytical insights using the ‘as-is’ customer data. Examples of such enrichments are – deriving customer behavior such as a ‘discount buyer’ vs. ‘full price’ buyer – derived from historical purchases and behavior. ‘Active’ visitor or an ‘engaged’ customer based on the customers interactions on e-commerce and digital campaigns. The analytical tags maintained for every customer vary based on marketing objectives, and form critical inputs for segmenting customers to drive engagement. Marketing plays a critical role in identifying data gaps and determine how the data should be enriched.

Phase 2: Deployment

This is where the rubber meets the road. Deployment phase is vendor led (since we are talking buy option) – once critical data (such as customer master) is loaded and processed in the system, marketing is given access to the data. Other customer data activities such as enrichment of ‘nice to have’ customer attributes and cleansing of low priority customers (as defined by business) are executed in an agile manner. This ensures business can use customer data early in the cycle – they can test and validate the results and provide inputs, without having to wait till the end of implementation when it’s too late.

  1. Data onboarding and ingestion: Getting the process and mechanisms to incorporate data sources and rules agreed upon in Phase 1 in place, to create the comprehensive customer data record
  2. Configure data feed into downstream marketing systems: This is to ensure marketing teams have access to create customer segments, extract customer lists and data for analytical and personalization purposes
  3. Set-up process for tracking customer metrics: This stage requires setting up a tracking and measurement processA critical but often overlooked component of a CDP implementation is to track the health of customer data and business metrics. Information such as total customers vs. active/ inactive customers, customer retention, acquisition and conversion rates are key metrics to track to ensure the program objectives are being met
  4. (Optional) Set-up customer engagement and campaign integrations: Not all CDPs include ‘engagement features’ – such as real-time interactions or recommendation engines that provide personalized product and offer recommendations to customers using preferred channels such as email, mobile app, online, SMS etc. B2C marketers see higher value from these optional capabilities, making it a prized component of a CDP

Phase 3: Hand over and training

The final phase in a CDP program is to ensure marketing and other customer-facing functions are trained and have access to customer data for their requirements. In addition, data/ engineering teams are trained on monitoring data ingestion and serving from the CDP on an ongoing basis.

The success of a CDP program requires establishing a governance team which comprises of marketing, IT and data engineering. Collectively, they ensure ongoing collection of new data, and verifying/ tweaking the applied rules such that they stay current and continue to meet business needs. By tracking the health of customer data and marketing metrics such as loyalty, acquisition, retention, conversion and engagement consistently, an organization can become truly customer-centric. A well-executed CDP program delivers on business goals quickly, be it controlling customer churn, increasing share of wallet or growing customer value.

Download CDP Handbook Now!

Move Over Nostradamus: Prescriptive Analytics Takes Control of Customer Engagement

“What next?” is a question most marketers ask themselves every day. Some base their answers on experience while others rely on the gut. However today we see a new breed of marketing professionals that are basing their decisions on evidence, thanks to new technologies and prescriptive analytics companies that help make sense of the seemingly random data.

In the increasingly competitive fashion retail industry, where little can be left to chance, while planning is critical, the course of action for every shopper must be decided in a split second to capitalize on the micro-moments.

While predictive analytics tools gives you a view of what’s coming, prescriptive analytics models tells you exactly what should be done to make the most of the situation.

The Goliath in the Room

Machine learning has taken decision-making in marketing departments to the next level. Predictions and recommendations are being made by matching customer profile with product attributes. Easy to do with a handful of products, but virtually impossible to manage when customer segments and product categories go through the roof.

In fashion, for example, a purchase goes far beyond the basic attributes of color, size, fabric, design, and price. Sticking to basic attributes such as ‘t-shirt, blue, large, polo’ isn’t going to make the sale; there are hundreds of product subtleties that need to be factored in. Placement of logo, sleeve length, the texture of buttons – these features can make or break the sale for today’s discerning customers. While these are obvious to a consumer the moment they see the item, it’s extremely tricky for the marketing analyst to get right.

Driving Customer Engagement with AI

Given the ever-multiplying number of attributes, decoding data into actionable insights is now best left to AI driven analytics. Large retailers can predict what customers are likely to buy, and then prescribe the most efficient route to close the sale.

Last year, UK based retailer ASOS announced a significant improvement in predicting Customer Lifetime Value (CLTV) in marketing through the use of AI. The retailer built a model that classifies a given customer as valuable, and potentially how valuable based on signals such as customer’s demographics, purchase history, returns history, and web and app session logs.

(Fore)Seeing Patterns in the Data

The biggest reason for using AI is its inherent ability to perceive a deeper understanding of context, customer preferences and how they make purchase decisions. AI-driven analytics enables retailers to dive deeper into consumer data, automating recommendations for customers, providing them with information that is relevant and meaningful. Gartner’s Hype Cycle for Retail Technologies, 2018 mentions cognitive expert advisors (CEAs) as a technology with high benefit, with the potential to improve customer engagement by making recommendations and aiding decisions.

Crucially, this ace up the sleeve of retailers can bring about customer loyalty by helping them find what they want quickly through a comprehensive understanding of their preferences.

Impacting Bottomline with Predictive Analytics

By accurately predicting the product-likely-to-be-bought-next, and the kind of promotion that will appeal to the given customer, retailers can immensely impact customer loyalty and drive sales.

Effective ‘next best offer’ systems rely heavily on AI and advanced analytics and can drive personalization at scale.

To learn more about how you can improve customer engagement using prescriptive analytics models, Download our guide to driving next-best-actions in Retail.

Download now

Riding the Rollercoaster: Your Approach to Customer Journey Marketing

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. Yet brands continue to spend an average of 88% of their budget for customer lifecycle marketing in awareness strategies.

Instead of driving new customers to the top of your funnel and encouraging them to make a purchase at the end, brands that understand and address the various stages of the customer lifecycle can achieve a greater impact.

For a fashion brand, this makes a lot of sense. A customer may become aware of your brand through a pop-up sale resulting in a $40 value to the brand. But if that customer continues to be engaged, and returns five times per year and with an average spend of $40 each time, then in five years, the customer is worth $1,000.

Customer Life Cycle Marketing

Customer Lifecycle Marketing for Retail Success

Understanding the progression of steps a customer goes through when considering, learning, purchasing, using, and maintaining loyalty to a brand is important to your retail success.

To learn how to craft the right strategies that will get a potential customer’s attention, turn her into a paying customer and nurture her to loyalty, download our paper, “Adapting Marketing to the Customer Lifecycle in Fashion Retail

Gartner’s 2018 Hype Cycle for Retail Technologies. What you need to know.

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.

  1. AI in Retail (Rating: Transformational)
  2. Algorithmic Retailing (Transformational)
  3. Cognitive Expert Advisors (High)
  4. Customer-Centric Merchandising and Marketing (High)
  5. Algorithmic Merchandise Optimization (High)

These ratings are a testament of invention and innovation in Artificial Intelligence, Advanced Analytics, and Cloud at Manthan. And a vision, to create context-aware, AI-powered analytics products that bring the true power of AI to every role in your business.

Manthan’s efforts have been focussed on bringing analytics-driven decision-making to the real user. To design sophisticated analytics products in a manner that everyone can use them. Which we believe, is critical to the businesses dealing with the real-time, connected customer.

We architected our products by re-interpreting 4 critical areas – Analytics Consumption, Algorithmic Processing, Solution Engineering and Data Management.

1. Re-interpreting Analytics Consumption

Our primary goal, as Gartner puts it, is to “blur the lines between human and machine”. This is what led to the creation of Maya – the world’s first AI-powered conversational agent for business analytics.

With this, analytics becomes easy to consume. In a simple, conversational format that remembers context and processes information in real time, based on the user’s intent and his flow of analysis. Maya is integrated with mobile, desktop and personal assistant devices and can be invoked anytime. It offers both general and role-based models for analytics consumers.

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

Hyper-Personalization: The Next Frontier – With Raj Badarinath & Mike Ni   Manthan Editorial Desk     February 25, 2020  E-commerce , Omni channel retail , retail customer experience Personalized experiences are always blissful. Be it a personalized workout routine at the gym or a spa treatment, it always feels like a luxury. Personalization today is not just in terms of including a prospect’s first name in an email directed to them. It is the next step in the era of Hyper-per...


The six steps to align marketing to customer journey in retail   Amit Rohatgi     April 24, 2019  Blog , customer engagement , customer experience , Customer Journey , Customer Life-cycle 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 , Merchandise Analytics , NRF 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 , Merchandise Analytics , predictive analytics in retail , Real time 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 , Merchandise Analytics , predictive analytics in retail , 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 , Merchandise 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 , Merchandise 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...


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