Articles in Category : Customer 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

Written by Varij Saurabh on October 29, 2018. Posted in Blog, Customer Analytics, Customer Data Platform, Omni channel retail, Omnichannel Retail, single customer view, Single View Of Customer

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.

Learn more about Manthan CDP++

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.

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What goes into a Customer Data Platform Implementation

Written by Rachna Manwani on October 29, 2018. Posted in 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

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. 1. 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
2. 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. 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
2. 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.

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
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
Set-up process for tracking customer metrics: This stage requires setting up a tracking and measurement process. A 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
(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!

The Identity Puzzle in Customer Data Management

Written by Sameer Narula on October 26, 2018. Posted in Blog, Customer Analytics, Customer Data Platform

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

In Hindu mythology, Ravana, the great scholar and demon king, has ten heads, symbolizing his various powers and knowledge. The heads were indestructible with the ability to morph and regrow. In their battle, Rama, the warrior god, thus must go below Ravana’s heads and aim the arrow at his solitary heart to slay him for good.

In modern times, the consumer is a bit like Ravana, not in terms of his evil designs but his multiple identities. Research states that an average consumer in US today is connected to 3.64 devices. With proliferation of a host of new age devices like smart speakers, wearables, connected homes and automobiles etc., it is projected that she could be connected to as many as 20 devices in not so distant future. Like it did for Rama, this poses a clear challenge for today’s marketer – how to navigate through the maze of these devices to identify and recognize THE consumer so she can be singularly, consistently and contextually engaged across her addressable touchpoints.

Industry research suggests that only a small fraction of consumer businesses can currently accurately identify their audience – hence the advent and rapid rise of Identity management solutions that help businesses resolve the identity of their audience into individual consumer identities and profiles. The size of the Identity solutions market is estimated to grow from $900 Mn currently to over $2.6 Bn by 2022, outpacing overall marketing investments growth

A recent Winterberry research survey indicates that about 50% of consumer businesses have intensified focus and plan to increase investments on Identity solutions. While segmentation and targeting on paid media remain the predominant use cases for consumer brands, cross-device and channel personalization plus measurement and attribution are expected to become areas of focus in near future.

Identity Solutions: The past, present and future

At its core, an identity resolution solution’s job is to continuously gather audience activity data from a disparate set of data sources, platforms and services to derive a cohesive, omni-channel identity and profile of each individual audience member. However, the approach has been largely siloed so far with marketing channel specific identity platforms and strategies. CRM databases as custodians of first party customer and contact information, have been the mainstream identity platforms for direct marketing activations, primarily over email or direct mail.

With the growth of digital marketing spend, Data Management Platforms (DMPs) that store digital audience behavior data to primarily support display ad buying use cases have come into prominence. However, their relevance is now questionable with walled gardens like Facebook and Google closing doors on them. The other growing channel of influence has been mobile data platforms to support mobile device & location-based engagement.

To overcome the limitations of a disconnected, multi-channel approach that current Identity solutions like CRM databases or DMPs are constrained with, the focus is shifting to emerging modern solutions like Customer Data Platforms (CDPs) and Identity Graphs. These offer a unified, cross-touchpoint and omni-channel approach towards identity resolution and linking, enabling a fully harmonized, single view of the customer to the marketer.

The Mechanics of Identity Resolution

Identity resolution system’s key job is to continuously collect audience related data from a variety of sources and put it through an ongoing process that resolves, generates and updates this data into discrete consumer profiles, which are then used by the business for various forms of marketing or other activations.

The process comprises of 3 key steps:

Data Management – Includes ingestion of disparate set of consumer data, both identity and activity related, followed by processing and storage of this data into organized repositories.
Identity Resolution – This is a crucial and complex mix of a deterministic and probabilistic process of deriving identifiers, matching, cross-referencing and linking to unique consumer identities followed by a validation mechanism to maximize the accuracy of the resolution process.
Consumer profile generation – This associates all identifiers, attributes and activities into a harmonized, holistic Identity Graph of the consumer, an individual or a household.

What makes an Effective Identity Management Solution: 5 Mantras

Ensure the Identity system is fed with data from a wide array of data sources. Not just device activity but also the applications behind to help drill past the device, cookie or pixel and reveal the real people behind them and their behavior.
As part of data management, ensure meeting consumer privacy rights and compliance requirements of industry norms like GDPR, CCPA etc.
Identity resolution should include a consistent, rule based deterministic match process to ensure high accuracy critical to support contextual, personalized engagement in direct marketing use cases
The deterministic process must be supplemented with machine learning driven probabilistic matching to expand the data set, and meet the requirement of use cases like social media or display ad marketing that expect a wider net but relatively less 1:1 personalization
The generated consumer profile, in form of an Identity graph, while having the requisite accuracy and timeliness, should go beyond the linkages to identifiers and attributes by including the desired insights to optimally enable marketing activation use cases

Download CDP Handbook Now!

Move Over Nostradamus: Prescriptive Analytics Takes Control of Customer Engagement

Written by Bhavna Sachar on October 5, 2018. Posted in Blog, Customer Analytics, customer engagement, Next best offer, next-best actions, Prescriptive analytics

“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.

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Riding the Rollercoaster: Your Approach to Customer Journey Marketing

Written by Bhavna Sachar on September 18, 2018. Posted in Customer Analytics, customer experience, Customer Journey, Customer Marketing, in store experience, infographics

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”

Why Customer Data Platform Is A Must Have In Retail Today?

Written by Bhavna Sachar on August 30, 2018. Posted in Churn management, Customer Analytics, Customer Data, Customer Insights, Customer Journey, Customer Life-cycle Marketing, Customer Marketing

With an array of options at their disposal, customers are becoming more and more demanding with each passing day. Expectations are high and if you cannot meet them, they will move on and find another brand to lavish their time and money on.

To put it simply, brands must pay more attention to its customers to deliver an experience the customer will want to return to, again and again. A thorough knowledge of the customer and his/ her preferences are therefore mandatory, as it has become essential to provide every customer with a very personalized experience.

The problem of isolated customer data

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. In addition, information contained in sources such as social media, mobile apps, email, SMS, POS, coupons, loyalty programs etc. add to the barrage of data.

Having all this data is pretty useless if the retailer is unable to connect it and utilize it to make customer-centric strategies and decisions.

Gaining a single view of your customers

Creating a unified view of the customer requires data cleansing, curation, resolution, and transformation. It is also imperative to provide seamless access of customer data to business users without requiring IT intervention.

Nothing is more important in retail today than a single view of the customer, irrespective of where she chooses to do business with your brand.

Consider a hiking enthusiast who buys shoes online and talks about how excited she is about the new shoes and her upcoming trip. She may then later visit the store to buy a jacket and nylon pants. She also redeems her loyalty points with a coalition partner brand to rent backpacking camera equipment.

The amount of information around this one customer alone is a complex amalgam of data. By factoring in more information (such as Instagram feed as to where she is hiking, her lifestyle), a clear, unified 360-degree view of the customer can help retailers and their partners know what products and content to promote to drive conversions.

Enter the Customer Data Platform: A business managed a system that brings together a company’s customer data from multiple departments and channels to enable customer data modeling and optimization of timing and nature of marketing messages.

How retail businesses benefit

Let’s look at some specifics of how a Customer Data Platform can help retailers get results:

Increased revenue from existing customers through personalized customer engagement and special offers based on individual preferences.
Improved customer satisfaction through improved customer focus – the better you know your customers, the better you’re able to anticipate and give them what they need.
Optimized ad expenditure by knowing exactly where a customer is in his purchase cycle, and intelligently targeting them with relevant messaging.

All of the above have a direct and immediate impact on both revenue and profitability of a company, making the need for customer data management real and urgent for retailers.

Data science is today a game changer in marketing, and the importance of having the right Customer Data Platform in place becomes even more apparent.

Manthan view of CDP

We define CDP as the core data infrastructure that can ingest, manage and serve data. Manthan Customer360 is a CDP that also houses analytical and data science capabilities such as segmentation, look-alike predictions, data exploration, micro-segmentation, and self-service analysis.

To learn more about how a CDP can serve your retail business beyond customer analytics – operations such as assortment planning, pricing etc., download our Customer Data Platforms handbook

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

Written by Ajith Nayar on August 30, 2018. Posted in Blog, Customer Analytics, Merchandise Analytics, Supplier Collaboration

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.

AI in Retail (Rating: Transformational)
Algorithmic Retailing (Transformational)
Cognitive Expert Advisors (High)
Customer-Centric Merchandising and Marketing (High)
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.

14 Metrics that Impact Customer Churn in Fashion Retail

Written by Bhavna Sachar on August 27, 2018. Posted in Churn management, Customer Analytics, Customer Data, Customer Insights, Customer Journey, Customer Life-cycle Marketing, Customer Marketing

Fashion retailers have been seeing disruption to their businesses for over a decade. From digitalization and the “Amazon effect”, to subscription models and fast fashion, most fashion brands have had to operate at a cut-throat level of competition in order to just survive. In this situation, brands are particularly impacted by the loss of a customer. Not only do they experience a direct loss of revenue, but in an industry where social influence and peer reviews have a high impact, there is a cascading effect through loss of referral business. This can, in turn, impact future customer acquisition costs and marketing efforts as well. 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 neutralize it.

Breadth of Purchase (cross-category): Breadth of purchase refers to the variety of products that a customer buys. Lower cross-category purchases can indicate customers who are not deeply engaged and may simply churn for a better price point.
Customer Complaints: Who is complaining and why? Measuring this metric can help you know how close a customer is to churning. Complaints that are common to a category or product can be early indicators of larger concerns. Additionally, mentions in poor service, in-flexible returns/ exchange or frequent complaints can indicate that a customer is looking for other options.
Feedback Scores/ NPS: Churn is closely tied to customer satisfaction, so Net Promoter Score (NPS) which measures the willingness of customers to recommend a company’s products to others can be used to understand high or low levels of customer brand loyalty.
Repeat Purchase Rate (RPR): This tells you the effectiveness of your marketing strategies and retention programs. Brands can isolate one-timers and focus their energy on making them visit again, which can yield higher gains than trying to acquire new customers. The revenue from a single repeat shopper is equal to that of around 6 new customers.
Repeat Purchase Probability: Different from the RPR, the RPP is closely related to the churn rate, as customers less likely to make another purchase are more likely to churn. In fact, studies have shown that conversion rates of repeat customers are much higher – a customer who has purchased twice in the past is about 8X more likely to convert than a first-time shopper.
Customer Lifetime Value: Customer lifetime value is the future profit your business can earn from its relationship with a customer. This is based on past purchasing behavior and their likelihood to remain engaged with your brand; it is a key metric to identify your top acquisition channels and optimize your customer interactions in a way that prioritizes your best customers.
Recency: An important and often under-rated metric, recency is the time since the last transaction. By segmenting customers on recency, you can detect the impact of marketing on purchases, and filter customers most likely to churn. This often isn’t as straight-forward as it seems, because in fashion customers don’t return at fixed intervals. However, if a customer that always visits during events such as Back-to-School or Black Friday sale doesn’t show up, it is a cause for concern.
Average order value: Average order value measures the average amount of money a customer spends per purchase or average basket value (size) per order. By understanding the basket size trend, retailers can spot anomalies in purchase behavior and identify if the customer is showing early signs of churn.
Product Reviews: Product reviews are often an indication of customer satisfaction. Consistent poor reviews could indicate that a customer is in search of other options, and also create a wider negative impact, making it harder to acquire new customers.
Profitability Per Order: In addition to business success, a high PPO can also indicate if only higher margin products and full price merchandize are being sold, giving you information on whether your tactics are successful or whether churn is eroding your profits away.
Purchase Frequency: How often the average shopper makes a purchase indicates how engaged they are. A customer returning frequently for their needs indicates you are top of mind and are better positioned to drive higher revenue from them.
Time Between Purchases (TBP): The gap between purchases within a one-year period is the time between purchases (TBP). Knowing this value can you give you insights into buying patterns and segmentation, enabling you to better understand which groups are churning.
Redemption Rate (RR): The percentage of loyalty rewards being redeemed is your redemption rate and a direct indication of your customer engagement levels. The average rewards program sees a redemption rate of 14%, and retailers that are too far off from their benchmarks need to when to act to tailor the program.
Product Returns: Like customer complaints, measuring product returns can indicate which categories are likely to make a customer churn. A high return rate from an individual can build up dissatisfaction with the brand and reduce their chances of shopping again. Additionally, social feedback on product quality shared with peer groups can have a high impact on churn.

The importance of controlling churn Fashion retailers find it especially hard to identify customers at risk of churning – shoppers don’t buy at fixed intervals and loyalty is at an all-time low. Customers that seem to be disengaged might still be interested in your brand, just not reached their re-buy period yet. Investing in customer retention programs can make all the difference to business growth. According to Bain & Company, increasing customer retention rates by a marginal 5% can increase profits by a whopping 95%. It is therefore critical to study the behaviors, interactions and experiences that are driving the customer relationship, to measure churn and take effective steps to minimize it. Manthan enables marketers to execute comprehensive churn management programs. Custom-built for the retail industry, our responsive algorithms, and AI-driven execution capabilities provide marketing teams with the insights and control they need, to proactively engage with at-risk customers. For more information, read AI Driven Approach to Boosting Customer Retention in Retail

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

Written by Santosh Kumar on August 14, 2018. Posted in Blog, Customer Analytics, fashion merchandising management, Fashion Retail

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

Written by Bhavna Sachar on July 30, 2018. Posted in Blog, Customer Analytics, fashion merchandising management, Fashion 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:

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
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.
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.
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.).
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.

Conclusion

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.

5 Ways Fashion Businesses Can Increase Traffic – Online And In Store

Written by Santosh Kumar on July 9, 2018. Posted in Blog, Churn management, Customer Analytics, Customer Life-cycle Marketing, Micro-segmentation, Next best offer, Path-to-purchase

It’s 2018. What’s the most revolutionary development in fashion over the past few years? Is it the check and plaid mania? Or maybe the shirtdress or the oversized, lightweight dress? Maybe it’s the apron and baby doll dresses that have entered the summer wardrobe (from the Halloween wardrobe)? The real answer to that question is data or much rather, the explosion of data that fashion houses are exposed to and the millions of ways that data can be interpreted and acted upon over the last few years. Shoppers these days are connected all the time, mobile ubiquity provides them the freedom to browse, research and shop where they want. You no longer need a customer to fill out a survey post their purchase, at a store, to understand who they are or what they like. You are now able to effectively build a profile of a customer using third party data sources like Instagram likes, for example, and get a pretty good understanding of their lifestyle before marketing to them.

If you looked at the fashion landscape and who’s really making the moolah, names like Amazon crops up. This is not right? They are not blue blood fashion retailer? Yet they are able to sell more fashion products than almost 40% of fashion retailers put together? They don’t have Giorgio Armani or a Coco Chanel suggesting what is the right outfit for every single customer? They don’t have a celebrity makeover team that is suggesting what color top a mother needs to wear to a soccer game? What they are good at is collecting data, deriving insights out of that data and acting on that data by putting the right product in front of the customer, at the right time, on the right channel.

You would assume that in this day and age, with all the power that data brings, increasing traffic to the store and getting customers to buy online should be fairly simple. Not really. The top challenges that fashion retailers are facing today when it comes increasing traffic are similar across the board and can be categorized into four main buckets:

They don’t understand the customer and what interests them. They are simply unable to the catch signals online and off.
Their customers don’t get context-aware, personalized communications.
They are not giving enough reasons for the customer to keep coming back. Their loyalty campaigns are limited to a quarterly newsletter blast for example.
They are unable to identify when a customer is ready to buy and lure them with offers that might attract that specific microsegment of customers.

To overcome these challenges the basic investment that’s needed for a fashion business is a Customer Data Platform (CDP) that gathers information from various sources like POS Systems, Online activity, Loyalty programs, CRM systems, third party data sources, etc. Once you have that in place, here are 5 ways you can exploit the insights and predictions to increase traffic to your store (online and off).

Customer Life-cycle Marketing – Get the basics sorted. Create segments that you can use to put a label on every one of your current/ past customers. This could be based on where the customer is in their relationship with the brand, for example: just acquired, first-purchase, repeat buyer, loyal, wavering, churn.
Micro-segmentation – Basic segments alone are not enough in the age of data. You need effective customer micro-segmentation based on lifestyle, life stage, behavioral, demographic and campaign responses to have an accurate view and understanding of each cluster. This would help you create highly relevant lists to run specific campaigns. There are some strong products out there that can help you do this using propensity modeling. You can forecast the future value of customer, rank and prioritize the customers and even allocate marketing budget using these advanced technologies. Make use of them.
Effectively recommend the next-best offer – You can do this based on affinity between categories, brands and customer segments. For example, if over 40% of 30+ women bought a silver color sunglass with white oversized shirts, that’s a good indication for the system to throw up that insight to you or automatically recommend the silver glasses as a ‘complete the look’ recommendation online. If you can get a software that can identify cross-sell opportunities, personalize content/apps based on stage automatically using AI, even better.
Path-to-purchase – NPS (Net Promoter Score) is not just a fancy word thrown around in the boardroom. It really does make sense, in every business. Identifying every customer segment’s journey/ path to purchase and chalking out all the touchpoints that led to a purchase is a key element of increasing traffic. Not only can it help you shorten the buyer’s journey but can help optimize marketing spend as well. Win-Win.
Churn management – The last hat tip is to effectively manage the customers that are about to or have churned. The lazy approach, taken by most fashion businesses, unfortunately, is to include them in a newsletter group that regularly gets updates on a sale or some branding comm. At this stage, the business has effectively given up on these customers in my opinion. The first step to effective churn management is to identify customers that are likely to churn based on visit history, purchase/ lack of, campaign response and overall engagement. You can then build rules and logic to entice them with offers and promotions that are most relevant to their micro-segment, and in today’s world, these can be done automatically by the machine!

Manthan named a Strong Performer in Forrester’s Customer Analytics Wave™ Q2 2018

Written by Bhavna Sachar on June 12, 2018. Posted in Blog, Customer Analytics, Customer Data, Customer Insights, Customer Journey, Customer Marketing, Customer Segmentation, Forrester, Loyalty, Market Segmentation, Marketing Analytics, Marketing Events, Marketing Measurement, Marketing Strategy, Personalization, Real-time Personalization

Modern CMOs are striving hard to build relationships with their customers that are deeper than those with their competitors and are transforming their customer marketing practices. They are moving away from mass marketing to targeted, hyper-relevant content, across all customer touch-points in the moment of truth. To consistently succeed with hyper-relevant personalization, CMOs are looking to establish rich capabilities for Customer Analytics, Data Management and Omnichannel Execution.

Having served retail and consumer businesses for over 14 years, Manthan brings a unique mix of deep industry experience and data science expertise that has translated into specialized customer analytics offerings for every segment from grocery to fashion, specialty and restaurants and for every format from department stores to convenience stores.

Manthan has now been recognized as a strong performer in The Forrester Wave™: Customer Analytics, Q2 2018

To assess the state of the customer analytics solutions market and see how the vendors stack up against each other, Forrester evaluated the strengths and weaknesses of the nine most significant customer analytics solution providers. These vendors were evaluated against a comprehensive set of 39 criteria, which were grouped into three high-level buckets: ‘current offering’, ‘strategy’, and ‘market presence’.

Forrester states that Customer Analytics solutions are “a new breed of analytics technology” that “perform common customer analytics techniques automatically, emancipating insights from customer data with ‘do-it-for-me’ (DIFM) capabilities”. According to Forrester, Consumer Insights professionals and their stakeholders “want easy and immediate access to insights”. Those adopting customer analytics solutions benefit from ‘Business user accessibility’, ‘speed to insights and action’ and ‘single view of the customer’. We couldn’t agree more.

A holistic understanding of the customer behavior and responding to their motivations and interests is critical for consumer facing businesses today. Manthan’s customers across retail are using Customer360 and TargetOne for use cases such as personalization, multi-channel marketing, location-based targeting, and product recommendations.

“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

According to Forrester, rapid deployment to match user needs, automatic insight identification, strong model monitoring and analytical transparency are key factors professionals should look for when considering a Customer Analytics solution.

“Manthan’s robust customer data model underlies prebuilt KPIs and predictive scores, which retailers and other direct-to-consumer brands find alluring.” -The Forrester Wave™ Customer Analytics Solutions, Q2 2018

This market is characterized by a high level of complexity with many different technologies and providers. Manthan differentiates itself by helping business users consume the insights out-of-box without having to deal with complex algorithms. Some of Manthan’s customers across the globe include Comcast, Payless, Les Schwab, Future Group, Big Y, Robinsons Supermarkets, Chedraui, Consum, Yum Brands, Charming Charlie and FairPrice. This recognition definitively puts Manthan on the Customer Analytics map, as it further elevates how it serves its customers; to understand, engage and wow their end-customers.

Customer Analytics

Advanced Analytics in The Restaurant Industry

Written by Santosh Kumar on May 17, 2018. Posted in Analytics, Customer Analytics, Marketing Analytics, Restaurant Marketing, Restaurants

The fight over which restaurant gets to satisfy your appetite is surely heating up. With ~$800B sales last year, this is an exciting space to watch, especially now. Customer expectations are at an all-time high and the consumer behavior is simply changing. Before we jump in, let’s look at what makes the restaurant business so unique.

Peculiarities of the Quick Service Restaurant Industry:

The average ticket value is low: Think about it this way, if you picked up all the bills from the last 10 times you visited a pizza chain and added it up, you probably won’t go beyond $1000. Juxtapose this with an apparel retailer and you would see that their average ticket value by itself is probably more than $1000.

Visit frequency is higher and cyclical: Compared to other retailers, the restaurant industry might see the same customers 8-10 times in a period of 6 months and before the frequency drops. Guest frequency is one of the most important metrics that a restaurant analytics tracks.

Size of the meal matters: Apart from guest frequency, the moolah is made by maximizing the size of the order. A large coke with the burger? Small fries to go with that? You can try combo 1 and get an extra burger by paying $2?

Customer tastes don’t vary much: Fashion trends may change with time, but a guy who likes pizzas, likes pizzas, for a long time. (Until he signs up for personal training at the local gym at least). A BBQ enthusiast might always order the same slow-cooked pork roast sandwich as opposed to a vegan who might order a vegan sandwich, at the very same outlet. And this taste does not change much over time.

The restaurant industry is unique for sure, but if you look closer, the mandates from their C level can be boiled down to three simple goals:

Increase Meal Size
Increase Guest Frequency
Decrease Customer Lapsation

Gone are the days when oversized chicken costumes and cigarette smoking men, dressed as clowns were deployed as strategies to attain these goals. In today’s day and age, there are advanced tools, smarter analytics, and intelligible information that are helping restauranteurs devise effective strategies. With an increasingly digital-savvy customer, in a multichannel ordering environment, there is no dearth of data that is available for restaurants. The real trick is, however, to make sense of all these nuggets of information and derive insights that positively impact the Net Promoter Score (NPS).

Here are some examples of how some leading restaurant chains have put their data to work:

Identified Taste affinity clusters – A large pizza chain recently used millions of data points to arrive at 10 primary segments of customers and looked at their past purchase behavior to identify taste preferences. They identified that the large pizzas were primarily ordered by dine-in, family segment as opposed to the regular size pizzas which were typically ordered by office goers/ singles who visited the store. They even identified the times at which a certain pizza gets sold more and identified cross-sell items based on purchase behavior/ taste of a segment.

Buying behavior analysis– A burger chain took a different approach. They looked at purchase behavior across different channels to identify which menu items can be added to the combo for someone who orders through a mobile device as opposed to someone who prefers to visit the restaurant. They even used advanced analytics to get a single view of the customer by integrating their POS, mobile, web and social data to identify the customer and ensured that their messaging was consistent across all channels.

NPS and Feedback Analysis – A Chinese food chain used advanced analytics to integrate all the channels that they received feedback in (mobile, at location, social) to get a single view of the customer and layered it up with sentiment analysis. They used this data to give each customer a lapsation score which was then used to target them with unique offers depending on their lifecycle.

Store location analysis – Restaurant predictive analytics models were used by a coffee chain to identify the probability of a new store succeeding in a specific location as opposed to a location down the street. They identified pockets of demand and the model prescribed a set of potential locations in a given geographic area. They then used this data to score and rank comparable locations to determine the best location and format for the new store.

With the onset of advanced analytics in restaurant industry, the real question now is: As a customer, do they know what you are going to eat before you do?