Articles in Category: Customer Analytics

Automation and Augmentation of Retail Data and Analytics | NRF 2019

For over a decade, retailers have been trying to democratize analytics across their business units, departments and people, but it always seemed like a distant dream. With the use of artificial intelligence, it has become possible to smartly blend analytics into the very fabric of your retail business. Retailers can leverage AI-augmented retail data analytics that is context-aware, has all the insights needed for prescribing the right actions, and can even implement your decisions.

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. Today’s systems have the capability to sense what is happening within your store locations as well as departments of the entire retail business, develop focused insights about any operation, predict the upcoming challenges or opportunities, prescribe the best way to move forward, and then execute the decision. Such an analytics model, that presents an augmented AI experience to retail staff at any level as well as to the customers, is what is needed in the near future.

It’s time to make a transition to an algorithmic business

Now that most retailers have transformed themselves digitally, the next big step is to transition into an algorithmic business; one that uses AI to make the analytics system more aware and intelligent, and therefore auto-processes many of the tasks humans do. Machine learning based systems can learn what a user typically analyzes, what patterns in data they typically solve, and what business processes or goals they usually drive.

Gartner’s Hype Cycle for Retail Technologies 2018 predicts that, “In the next couple of years significant benefits will emerge as retailers absorb the impact of AI as part of an algorithmic retailing strategy. Algorithmic retailing takes a broader view of use of mathematical algorithms, deep learning, smart data discovery and other advanced analytic capabilities to make major contributions to the effectiveness of the retailers’ decision-making processes.”

With augmented AI solution, users can specify the expected outcomes. For instance, if they would like to improve sales of a specific product line while protecting margins by at least X%. Earlier, they would typically use the enterprise BI and Analytics tools, set data filters as they seem fit, use the dashboards to drill down additional details in order to get the specific insights to support their decision – and doing all this required a certain level of expertise.

But today, the AI-based system can take in your final goal as an input, then automatically processes data, recognize opportunities among the various tactics businesses use to improve sales by modeling the outcome against variables like product, price, availability, and customer preferences to recommend the best action to execute. What’s more; once you decide upon a suitable action, the solution would implement that decision for you. With many business contexts already modeled into the analytics system, it can function as a perpetual prescriptive engine for every process in retail and CPG – and for every individual in the retail organization.

How can it help retailers?

In today’s hyper-complex retail environment, retailers come across a wide range of crossroads every day, where they need to choose which path is perfect for their business. The data and information are all there, but what is still lacking is an ability to understand business context, identify anomalies that need attention, correlate all that data, predict outcomes, and prescribe the best decision or action.

Today’s AI-augmented retail data analytics solutions are autonomous, omnichannel entities that can help retailers execute repetitive, data intensive tasks at speed and scale. It can help retailers with resolving business challenges and taking decisions pertaining to all areas of retail, including: new product forecasting, inventory management, in-store engagement, real-time personalization, assortment optimization, promotions, campaign management, store operations, price markdowns, loyalty management, and a lot more.

This AI-based retail data analytics solution knows what your customers want, knows your manpower needs and inventory levels, can sense winning product combinations, categories, and promotions, and keeps getting smarter over time as more and more decisions are implemented.

How does it work?

There are three vital pillars needed for retailers to build an AI augmented environment that would democratize analytics across their business. These include: machine-driven data management and algorithmic insight generation, AI-augmented analytics process automation, and simplified user-interaction through conversational analytics.

The system must have an AI powered platform that ingests and unifies data and prepares it for analysis based on the context. It should offer a friendly user interface that humanizes interaction through natural language – so the users, just need to talk to it and the intuitive analytical system would start churning recommendations immediately. Once a question gets asked, it understands the context, processes the data and uses advanced algorithms to machine-analyze and prescribe the best action for a business outcome by considering decision contexts and simulating potential impact.

Welcome to the future – A Store That Knows

The stage is all set for AI to make a transformation in the way retailers operate. Algorithmic retailing will enable retailers to optimize sales for specific segments and categories while providing unrivaled operational efficiencies by automating repetitive and complex data-driven tasks. AI could be implemented for optimizing the entire retail business so that it operates at peak efficiency levels.

Gartner’s Hype Cycle for Retail Technologies 2018 estimates that, “up to 50% of retailers have adopted some form of algorithmic optimization application, and expect algorithmic retailing will grow from its current penetration relatively quickly.”

A Store That Knows, is a concept devised by Manthan Systems, and will soon become a widespread reality in retail. Manthan is at the leading edge of this evolution and is today enabling the underlying technology capabilities for several retailers. Manthan is pioneering innovations in analytics by using the power of AI to enhance decision-making across various dimensions in the technology stack, including data management, insight generation and analytics consumption.

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Analytics & Insights: Interview with rue21’s Chief Analytics Officer, Mark Chrystal

rue21 has been making news for its investment in analytics in order to transform itself into a more customer-centric business.

We caught up with Dr. Mark Chrystal, Chief Analytics Officer at rue21 to understand more about how he perceives the role of analytics in retail today, his upcoming talk at NRF’s Big Show and the future of retail.

MANTHAN: In your role as the Chief Analytics Officer, what would you say is the biggest challenge facing rue21 in 2019?

MARK: The biggest challenge I face is the ability to explain what is happening in the industry and more importantly, with our current, lapsed and potential customers. My job is to help the business navigate the environment and provide insights that help chart a course to success. This is particularly challenging in the current retail environment and for a company that is in the midst of a turnaround.

“We are now seeing analytics embedded across each functional unit as means of explaining what is happening, where it is happening, and how best to respond.”

MANTHAN: In your 20 years of retail experience, what have you noticed about the changing retail industry’s attitude towards analytics?

MARK: When I started in retail, analytics was being thought of as secondary to the success of a retail business. Analytics groups, if they did exist, were often in their own silos away from the day-to-day running of the business. At that time, most of the CEOs and head merchants across retail were trained based on having direct face-to-face interaction with their customers, and therefore thought about the business through a much more qualitative micro-level lens.

With the advent of eCommerce and social media and social influencers, the environment is far more diverse and complex than it was twenty years ago. We are now seeing analytics embedded across each functional unit as means of explaining what is happening, where it is happening, and how best to respond.

“Retailers need to employ real-time analytics to help them identify emerging themes, issues and opportunities.”

MANTHAN: AI promises to make data-driven business processes more intelligent. What are the top use cases you think might have big impact in retail today?

MARK: The top use cases for AI today, are in the automation of rote tasks, and in the identification of patterns and opportunities that are not as readily discernable via other analytical methodologies or business processes.

MANTHAN: We understand you’ll be speaking at NRF. What is the product paradigm shift going to be about?

MARK: I will be speaking at NRF about the shift within retail towards data-driven decision-making and organizational culture.  The presentation will focus mostly on how merchandising functions need to, and are, making this shift.

“Retailers need to create organizational cultures that are capable of interpreting real-time insights and taking action on those insights.”

MANTHAN: As enterprise and customer data continue to grow and customer journeys evolve, how can retailers keep up with sensing, analyzing and responding to opportunities potentially unfolding every day? 

MARK: I believe retailers need to employ real-time analytics to help them identify emerging themes, issues and opportunities with their customers and competitors. This means having models tuned to real-time analysis, alerts and insights across the retail footprint. This also means that retailers need to create organizational cultures that are capable of interpreting real-time insights and most importantly, taking action on those insights.

Most retail organizations have not evolved to this point yet and are still grappling with the change from the old merchant model to the model that modern customers clearly demand. The best retailers understand this, have made those changes, or created those types of cultures at inception and they are reaping the rewards.

MANTHAN: Thank you, Mark!

rue21 has selected Manthan, a leading provider of cloud analytics and artificial intelligence solutions, to help advance its analytic capabilities. The retailer will be rolling out Manthan’s Customer Data Platform, Customer Analytics and Enterprise Retail Analytics solutions to gain insights within the business and better connect with consumers.

Visit us at NRF 2019, Booth 4719 for more information on how Manthan can help you use analytics to become a future ready retailer.

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[Infographic] Why will you attend NRF 2019?

This year at the NRF Retail Big Show 2019, Manthan will be showcasing The Store That Knows

This AI-powered, omnichannel entity offers analytics and insights 24×7, giving you smart recommendations while implementing your decisions. All in natural language.

And if that’s reason enough, take a look at the following infographic to see who else is likely to be at the NRF Big Show.

Supplier Collaboration

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How to Assess Your Data Requirements When Implementing a Customer Data Platform

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

Customer data is no longer just an analytics requirement, or something used to send out marketing messages – it is the source of critical competitive advantage for a B2C business.

Truly customer-centric organizations use customer data for all aspect of business – from guiding product roadmap and categories, designing rich experiences, pricing their offerings right, creating the right customer service interfaces, deciding sales channels and even to make the right hiring decisions. Modern marketers use these customer insights to form a unified understanding of every customer and create contextual experiences that strengthen the relationship with the customer, ensuring loyalty and higher word of mouth.

Every company today collects more data about their customers that they use. They can also easily acquire additional, reliable data from data companies, and each byte presents insights that were not available before.

As a first step when deploying a Customer Data Platform (CDP), know what use cases you want to enable – this decides what data needs to be brought together. At this stage, assess your present data and what you need to acquire. Controlling customer churn, lifecycle marketing, and marketing attribution all require certain unique data sets.

The use cases and user types will dictate the format to store data in, the frequency of refresh and ingestion (real-time or batch) data, transformation required and responsiveness.

For example, in an e-commerce retail business like Amazon, purchase history is needed to provide personalized recommendations to customers. While digital data such as web-browsing/ app-browsing, items added to cart, saved for later, searched or viewed is captured, there is value in plugging in additional information such as income bracket, size of family, lifestyle, nature of work, types of vacations taken. This provides deep insights into what the customer would like, and the customer is pleasantly surprised with the retailer’s next-best product recommendations.

Organization owned customer data from the CDP foundation

Let us start with the data you have. If you have a Customer Relationship Management (CRM) program, provide any level of customer service or have a functioning loyalty program, then you have a solid foundation for your customer data platform. The CRM and loyalty systems can provide insights about demographics, purchase behavior, loyalty, and customer lifetime value and also contain the basic identity details.

But to do this right, it’s necessary to have a single customer record across all touchpoints by removing duplicate entries and resolving mismatches, that are created due to multiple reasons: incorrect data entry, different name variations of a customer, customers with multiple loyalty cards, change in their address etc. (Read more about solving the customer identity puzzle here). After de-duplication and linking customer records, you know the real size of your customer base and have more accurate profile snapshots.

Transactional and digital data generates actionable insights for strategic gains  

If your business engages your customers in more than one channel, for example through the store and e-commerce, or over mobile apps, that’s a new set of data streaming in, which can enhance your understanding of your customers. Again, it’s important to link customer IDs across different channels, functions and customer devices to have a holistic customer view, that does not vary based on who is accessing it and where.

Most businesses use outbound digital communication data for pointed interventions, but information such as search, browsing patterns, offer engagement, wish-listing, abandoned carts all tell you something about the customer.

An insight-driven organization focuses on this knowledge, rather than the data itself. If along with the CRM and loyalty data, you have these astute observations, it can power better marketing and experience strategies. Storing every click of every customer is not feasible and honestly, not required. The signals you get by analyzing these clicks, cookies, customer location, and device information during the digital engagement should be part of your CDP strategy.

External data sets to enrich customer attributes

Finally, bring in 2nd and 3rd party web, demographic, psychographic, location, buying intent data to further enrich what you know about your customers. This is the ‘test-use-evaluate and iterates’ part of your data strategy. Not all data providers are the same, they all start their data journey differently and may or may not work for your brand and your customer base. It’s also important to note that data degrades quickly – the data was good a few quarters ago might not be valid anymore. Customer interests and intents may have evolved – they may have changed jobs or had a major life-event. To find out what works for you, do small experiments with a good set of data partners and choose the one that works best for your current marketing and strategy needs.

If you are poorly utilizing the data you have captured, or not capturing certain essential data, you are losing important revenue opportunities. If data is the new oil, accurate customer insights are high octane racing fuel for your B2C business!

Download CDP Handbook Now!

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Retail Prophet Doug Stephens Interview: The store of the future won’t be a “store”

Changes are rampant in the retail industry. Every day we hear of both big brands (and small ones) closing shop or downsizing in an effort to stay lean and competitive. To understand in depth what’s happening in the retail landscape, we spoke to Doug Stephens.

Doug is the founder of Retail Prophet, and one of the world’s foremost retail industry futurists. The author of two groundbreaking books on retail, a nationally syndicated retail columnist, and over 20 years of experience in the retail industry, Doug brings together his unique perspective to provide Manthan with his insights on retailing, technology and consumer behavior.

MANTHAN: You speak of a sea change in retail and the trouble that’s coming for brick and mortar stores – can you explain this revolution?

DOUG: We have entered an era where media (in all its various form factors) has become “the store”.  Whether it’s a magazine ad I can activate with my smartphone, my connected appliances, my smart TV or my in-home artificial intelligence, media is no longer a call-out to visit a store – it is the store.  And this fact will only continue to escalate and permeate more aspects of our consumption.  Therefore, the role and nature of physical stores must change in order to adapt.  

Stores have to become places that move beyond the mere distribution of products and become enchanted spaces that distribute remarkable experiences.  The problem is, most retailers aren’t internalizing this reality yet.  Most store experiences remain unremarkable and it’s a fact that’s taking a toll on retail generally.  If we’re honest, the retail brands that are getting wiped out aren’t being mourned because they had in fact become irrelevant long before they died. 

“The retail brands that are getting wiped out aren’t being mourned because they had in fact become irrelevant long before they died.”

MANTHAN: What’s the role of technology in this revolution and which retailers are leading?

DOUG: Technology has essentially become the connective tissue in every aspect of our lives, whether we’re talking about business, leisure, commerce etc.  In retail, we know that upwards of 80 percent of all retail transactions are being influenced, to some degree, by digital.

The retailers that are leading are those that are essentially using technology as the mortar between the bricks of their customer experience.  They’re viewing technology as an essential means of removing friction from the customer experience as well as fortifying moments of experiential delight. Among the brands that I see as excelling are Amazon, Sephora, Starbucks, Wayfair – to name only a few.

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!

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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.

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

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
    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. 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.

  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 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
  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.

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The Identity Puzzle in Customer Data Management

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:

  1. 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.
  2. 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.
  3. 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

  1. 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.
  2. As part of data management, ensure meeting consumer privacy rights and compliance requirements of industry norms like GDPR, CCPA etc.
  3. 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
  4. 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
  5. 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

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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.

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