Here are a few predictions and perspectives from industry experts to learn from and be smarter this year:
The Promise of Artificial Intelligence (AI) and Machine Learning (ML) Continues on
There have been repeated predictions over the last couple of years touting a potential breakthrough in enterprise use of AI and ML. This year is no different as the potential benefits from adding some kind of intelligent AI/ML layer to software emboldens more organizations across industries to adopt these technologies.
ML and predictive analytics are leveraged to suggest next-best-actions for sending relevant and timely information to customers and finding opportunities for up-sell and cross-sell. Insights like churn propensity, life-time-value, preferences and abandonment rates can be delivered to relevant teams, along with recommended actions that allow them to capitalize on this information.
Effective May 25, 2018, the European General Data Protection Regulation (GDPR) will force organizations to meet a standard of managing data that many won’t be able to fulfill. They must evaluate how they’re collecting, storing, updating, and purging customer data across all functional areas and operational applications, to support “the right to be forgotten.” And they must make sure they continue to have valid consent to engage with the customer and capture their data.
Meeting regulations such as GDPR often comes at a high price of doing business, not just for European companies, but multinational corporations in an increasingly global landscape. Companies seeking quick fixes often end up licensing specialized technology to meet such regulations, while others resign themselves to paying fines that may be levied, as they determine that the cost to fix their data outweighs the penalties that might be incurred.
With security and data breaches also making high-profile headlines in 2017, it’s become an increasingly tough environment in which to do business, as the very data that companies have collected in the hopes of executing offensive data-driven strategies, weighs on them heavily, crushing their ability to be agile.
Customer-obsessed, Data-driven Retailers will Thrive
With the Cloud Infrastructure as a Service (IaaS) wars heating up, players such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure continue to attempt to outdo each other on all vectors including capabilities, price, and service.
To avoid being “Amazoned,” some retailers have even adopted a non-AWS Cloud policy. For most, however, it’s about efficiency and cost. Multi-cloud means choice and the opportunity to leverage the best technology for the business challenges they face.
Today’s Modern Data Management PaaS are naturally multi-cloud, seamlessly keeping up with the best components and services that solve business problems. Acting as technology portfolio managers for large and small companies who want to focus on nimble and agile business execution, these platforms are democratizing the notion of multi-cloud for everyone’s benefit.
The Deck will be Cleared for Accelerated Enterprise Digital Transformation
The business landscape is changing like never before. New revenue models, new competition, newer regulations and exceeding customer expectations are forcing organizations to rethink how they do business.
Digital transformation is one of the key initiatives for many organizations looking for ways to leverage digital technologies, become agile, more productive and above all, provide a connected digital experience for their customers. For digital transformation to succeed, a solid data management foundation is a must.
Today’s Modern Data Management Platforms as a Service (PaaS) seamlessly powers data-driven applications, which are both analytical and operational, delivering contextual, goal-based insights and actions, which are specific and measurable, allowing outcomes to be correlated, leading to that Return on Investment (ROI) Holy Grail, and forming a foundation for machine learning to drive continuous improvement. As an added bonus, multi-tenant Modern Data Management PaaS in the Cloud, will also begin to provide industry comparables, so companies can finally understand how they rank relative to their peers.
With the emergence of IDNs, ACOs and MCOs, the approach to healthcare is evolving. The focus is on overall well-being and quality of life, rather than a one-time treatment. This requires a new patient-centric approach, complete understanding of the patient’s needs, behaviors and preferences, and focus on building long-term relationships.
In this changing healthcare environment, a modern approach to data management that enables complete understanding of patients, physicians and other partners across all clinics and facilities, while guaranteeing HIPAA compliance is necessary.
Whatever the industry or business need, most enterprises will need to first focus on IA (Information Augmentation): getting their data organized in a manner that ensures it can be reconciled, refined and related, to uncover relevant insights that support efficient business execution across all departments, while addressing the burden of regulatory compliance.
Given the vast volume and variety of data that CPG companies manage, ensuring the accuracy and reliability of data is critical. All digital transformation and personalization efforts would fail if data underneath is of poor quality, siloed and delayed. Using machine learning within modern data management platform not only helps determine and improve data quality but also enriches the data with relevant insights and provides intelligent recommended actions for data quality and operational improvements. For example, if you are running a campaign for a major product launch, you can eliminate consumer profiles with low data quality (DQ) scores.
2. Be Agile with Multi-model Data Management
Using legacy tools built on relational databases are too rigid and inflexible, making it difficult to support the dynamic needs of a modern business. For example, adding new data sources or attributes to the customer profiles can result in costly data migration projects. Another challenge is the inability to manage the relationships between various data entities, such as people, products, organizations and places. Modern data-driven CPG brands prevent big data indigestion by using a multi-model, polyglot storage strategy to store and efficiently manage the right data in the right storage. It helps them deliver faster and higher business value from their varied data assets.
3. Leverage the Power of Multi-domain
With “single domain” Master Data Management (MDM), each data entity type has its own unique data store and business logic. On the other hand, a Modern Data Management Platform manages multi-domain (customer, products, stores, suppliers) master data along with transaction and interaction data, third-party, public and social data. Its graph technology makes it easy to describe and visualize complex, many-to-many relationships among customers, products, stores and locations for faster and reliable decision-making. For example, with the help of a graph, CPG brands can rapidly traverse links between consumers, products, purchases, and ratings to make personalized recommendations. They can also tell if the visitors and shoppers browsing their website are from the same household or not.
4. Uncover New Business Models
“Servitization” of products is commonly seen in consumer categories such as music (iTunes and Spotify) and books (Amazon Kindle) but also in business services such as Xerox moving from photocopiers to document services. Historically, CPG companies have been resistant to the move from products to services. Their relationship with their consumers has often been mediated via retailers. Modern data-driven CPG brands often bypass retailers and sell directly to customers (DTC). For example, Dollar Shave Club is offering a monthly subscription to deliver razors and other personal grooming products by mail. This gives them the opportunity to engage directly with their customers, to collect interaction data, and to expand their digital footprint.
5. Explore New Data Partnerships
Data is an enabler of innovation. To keep up with the rapid pace of digital transformation, CPG brands need to develop a culture of collaboration and pursue intra and extra-industry partnerships. They need to recognize that many new entrants are not simply additional competitors. Instead, they represent possibilities for completely new types of business models that over time will blur traditional distinctions between retailers and manufacturers.
6. Augment Decision Management with Artificial Intelligence (AI)
Data-driven CPG companies look at AI through the lens of three business capabilities: automating business processes, gaining insight through data analysis, and engaging with customers and employees. They constantly innovate and disrupt by embracing new technologies to meet the high expectations of consumers. A Modern Data Management Platform coupled with Machine Learning enables contextual information and helps consumer brands answer high-impact business questions such as – Will my customer buy this product or not? Is this review written by a customer or a robot? Which category of products is most interesting to this customer? And so on.
7. See GDPR Compliance as an Opportunity to Improve Customer Experience
CPG brands will be required to be more transparent about how they use consumer data. New regulations like GDPR and increased oversight has important implications in terms of regulatory compliance, product development and marketing messages. Moreover, there are increasing consumer demands for transparency on how companies perform when it comes to sustainability and corporate social responsibility as well as where products are made. A Modern Data Management Platform as a Service (PaaS) helps you create a complete consumer profile with full data lineage, governance, and workflows to continuously manage consumer rights and consents.
Consumer brands are facing unsteady growth, tightening profit margins, complex regulations, and growing competition from lower cost private label brands. Adopting these seven habits would help them reverse the digital curse, achieve hyper-personalized customer engagement, and stay ahead of competition.
DISCLAIMER: I have no inside knowledge into what may or already have been discussed, or data analyzed by either AMC Theaters or MoviePass. This article is purely based on my thoughts as a movie-goer, marketer, and product manager.
“AMC Theatres has threatened MoviePass with a lawsuit, less than a day after the subscription cinema service dropped its subscription fees to $9.95 a month, reports Variety. That means subscribers are able to watch one movie every day for a month for only $9.95. MoviePass would still have to pay AMC full ticket prices each time someone uses the subscription, though. An average ticket is priced at $9.33, so a subscriber would only need to attend two movies a month to put MoviePass at a loss.
In 2016, the service started at $15 per month and ran up to $50 per month for unlimited movies in bigger cities. AMC, which is the largest theater chain in the US said in a statement that MoviePass’ model is unsustainable. The company argued that ticket prices below $10 a month over time wouldn’t be able to generate enough cash to operate quality theaters, nor produce enough income that would allow film makers to make movies of value.” – Source The Verge
I never knew about MoviePass as a subscription for unlimited movies. As a father of twins my wife and I barely get to go to the movies but once every 2 months, and it costs us an extra $100 in babysitting to go, and there’s nothing really compelling as far as “good” movies in our opinion, but I digress! Moviepass’ offer of $9.95 a month does seem to be very compelling, and ultimately very disruptive.
My first reaction in seeing that AMC is suing MoviePass for this action is to wonder out loud whether AMC had gone to MoviePass and offered to jointly analyze their respective datasets in order to see if there might be synergies in such an action.
An Outsiders Product Manager’s Perspective:
Showtimes of movies (beyond opening week of new blockbusters) are rarely full, meaning there is unused inventory in every single time slot
Pricing strategies to try and fill these slots don’t appear to have changed much beyond off peak time discounted ticket offers
Loyalty and rewards programs have now started to become more prevalent so efforts are ongoing to capture consumer profiles
Concession sales per customer are lucrative with a large popcorn and drink often costing more than the standard ticket (letting MoviePass fill shows to capacity could yield more in concession revenues than tickets itself)
Clearly I would need more data to find patterns and analyze this information to form the right conclusions. The steps would be to:
Form a Reliable Data foundation – leading to a 360-degree view of the consumer/movie-goer profile, with demographics, attribution, captured in part through AMC’s loyalty programs, but also could then be cross-referenced (Matched and Merged) with MoviePass’ subscribers to enrich both data sets.
Benefit from Commercial graph technology to find friends and family affiliations to drive offers (see marketing perspective later) to make it more of a social/group movie-going experience
Generate Relevant Insights – by bringing together the transactions processed via the tickets bought through MoviePass vs. walk-ins, and other avenues such as Fandango, promotions etc. Stanalone Master Data Management profiles are insufficient as the real valuable insights are in the transactions/behaviors exhibited by those movie-goers, and they need to be analyzed and seamlessly aggregated back into the master profiles for marketing segmentation
Deliver Recommended Actions – So marketing teams can jointly highlight how AMC and MoviePass could gain synergies from the increased traffic to theaters. Applying machine learning and data science to the reliable data foundation, not just at a macro-level, but to generate the right programs that can take advantage of the identified profiles, to drive more personalized experiences, and revenue-generating concession sales
Leverage Data as a Service – to securely share insights between AMC and MoviePass, preserve consumer privacy, and to bring in more data from suppliers of concessions to negotiate discounts and for synergies such as just-in-time ordering to improve margins
An Outsiders Marketing Perspective:
Once all this data is aggregated, made reliable, and analyzed, the joint market teams of AMC and MoviePass could work on promotions and programs using data-driven applications. With a Modern Data Management foundation they would be able to correlate Recommended Actions back to actual outcomes. Personalizing and improving customer experiences are just the cusp of benefits that can be realized. New business models that could easily be supported might include:
Making it more of a social experience –convert real-estate into Starbucks-like hangouts, with good coffee, wireless, and a place to meet. Offer better higher-end desserts so people come 30 mins before the movie with family and friends after dinner, or stay afterwards to chat about the movie and what they thought about it
Increased kids focus – more tie-ins and kids activities, pre- and post-movie with merchandise sales in a movie “store” with branded items tied-ins. Sales immediately after the event for instant gratification is the a way to command a premium over online sales and their lower prices
Given the fact that VOD, Netflix, Virtual, and Augmented Reality are literally right in the face of and challenging the movie theater going experience, AMC and other theater operators face being disrupted. A Modern Data Management Platform as a Service is essential to not only improve revenue, margins and partner better, but possibly survive.
How do you think the experience could be improved as a movie-goer?
As a Product Manager, how would you use data to gain better insights and possibly partner better. Have you used shared data and insights in similar situations between partners, or perhaps in M&A scenarios? Please share.
When was the last time you tried to access information and ended up spending an hour looking through multiple systems to find it? Even worse, when was the last time your data failed to give you the right information, at the right time to help you do your job?
In observance of Friendship Day, we would like to point out how the qualities of a lasting friendship have much in common with the effects of well-managed data upon everyday life:
A friend is someone who is there for you when they rather be anywhere else
Just as you expect your good friends to be dependable, reliable data is non-negotiable. It’s table stakes for any company who is looking to be data-driven.
How about a healthcare organization with siloed, duplicated hospital and patient data? You could probably use some support for improving data management efficiencies, and omnichannel engagement with your patients.
These sound like problems only a real friend could understand.
I get by with a little help from my friends
A real friend is always willing to lend a helping hand. Good data behaves similarly by providing you with consistent and contextual information that helps you with your daily activities.
Reltio’s data-driven applications combines reliable data, relevant insights and recommended actions into cloud applications. These enterprise data-driven applications give business users a consumer-grade experience, like LinkedIn, Google and Facebook.
Tailored to your industry and functional role, these data-driven applications can help you identify and reveal relationships among the people, products, places and activities you care about. They can also guide you with powerful visuals and intelligent recommendations that help you make better decisions as you complete tasks.
Workflow and collaboration capabilities also allow you to manage, curate and provide real-time feedback on data quality. These out-of-the-box workflow processes accept input and requests from business users, without using email. They also improve the accuracy and effectiveness of information through social collaborative voting, ranking and rating.
Everyone is a friend, until they prove otherwise
Your true friends have likely proven themselves to be trustworthy. Putting your data in the cloud involves a high level of trust in your data management provider.
Companies, like Reltio have gained the trust of their customers, through earning HITRUST CSF Certification status, for information security by the Health Information Trust Alliance (HITRUST).
A friend is someone who knows the song in your heart, and can sing it back to you when you have forgotten the words
Good friends always provide you with insightful information that you may not have thought of. Reltio’s Commercial Graph is like that.
Architected for today’s agile enterprise, it is built with a columnar and graph hybrid store to combine, relate and store an infinite number of attributes and relationships. Created for the purpose of delivering any type of data-driven application, for any business need.
It also handles operational and analytical functions within the same data-driven application. Covering every possible scenario, while giving you the ability to imagine your data from any perspective.
True friendship is like sound health; the value of it is seldom known until it be lost
The irreplaceable value of a true friend is not always apparent. True friendships, like sound health, need maintenance and continuous improvement to endure.
Data quality works similarly. One cannot tell whether data is valuable until it is analyzed. Oftentimes, the more maintenance and improvements that are made to the data, the more valuable the insights become.
Rather than mourn over lost opportunities from poor data, make sure your data scientists and analytical platforms have access to reliable data. Reltio Insights enables advanced analytics with machine learning, so that you can access accurate information for faster results.
Unlike analytics-only tools, Reltio’s bidirectional connectors bring aggregated insights back to master data profiles. Offering valuable recommendations for business improvement to the users of data-driven applications. Thereby making your data as reliable, consistent, trustworthy, insightful and valuable as your best friend.
As the Chief Product Officer at Reltio, my focus is on the data, and helping companies avoid being “Amazoned” (Informal definition: Brick and mortar stores under threat from online competitors). Rewind 1.5 years ago, an article by Phil Wainewright of Diginomica caught my attention. “Whole Foods Market teams with Infor to transform retail.” Credit Whole Foods executive vice president and CIO Jason Buechel with his vision to be more data-driven, and to create one source of the truth.
Whole Foods and Infor’s partnership was supposed to result in a next generation, cloud-based retail management system to transform its core operations. Infor, which hosts the software on Amazon Web Services (AWS), intended to make the capability available other retailers in the industry.
In the article, Buechel also told author Phil Wainewright of Diginomica that Whole Foods has carefully weighed the pros and cons of that cloud infrastructure being operated by Amazon Web Services (AWS), which is part of a company with which it competes in the online grocery market.
Kudos to Phil Wainewright for this article, because it called out what all Retailers are thinking today. The only way to avoid being “Amazoned” is to run on the very platform, Amazon Web Services (AWS), that can allow me to compete with Amazon.
Retailing executives are asking themselves am I okay with that? What are my alternatives? Clearly Whole Foods CIO Jason Buechel knew it was a risk worth taking. He may not have foreseen that Amazon would acquire Whole Foods, but he definitely knew that doing nothing was not acceptable.
As for the question, did Amazon buy Whole Foods for the groceries or the data? Clearly this is an amazing twofer. They get a physical presence that can help their delivery and Amazon Fresh efforts, but they also get the significant dataset of customers who buy groceries from Whole Foods. They now have the information to bring together a complete single view of the customer, from brick and mortar shopping to online purchases.
In the end, the data-driven takeaway to all retailers is not just evolve or be Amazoned, but do it fast because no company can afford to spend years working on digital transformation, when their very survival depends on better customer experience, better marketing, better omnichannel engagement, personalization and more.
Evolving from traditional to next-gen data management would mean taking these four key steps.
1. Tear Down the Silos Between IT and Business
As per the “State of the CIO 2017 Survey”, 59 percent of CIOs said IT collaborates with business units to build business cases for new MDM (and technology) initiatives, while only 24 percent of business leaders said that was the case. This indicates clear departmental silos between IT and Business. To bridge this gap, you must stop seeing your MDM as a separate siloed discipline, requiring complex IT infrastructure, processes, leading to months of design and implementation. Modern data management encourages an alignment and partnership between business and IT through an extremely user-friendly data governance interface, thus maximizing the ROI of your MDM investment. It has inbuilt collaboration and workflow capabilities to meet your enterprise’s governance framework and way of doing business. In addition, it allows IT and business users to provide feedback in a collaborative and controlled manner thus preserving valuable intelligence and competitive advantage.
2. Close the Loop Between Operations and Analytics
According to a recent McKinsey survey, 86% executives reported that their organizations were only somewhat effective at meeting the goals they set out for their data and analytics initiatives. The biggest culprit is a gap between analytics and embedding these insights into the operating model of the larger organization. Turning data into real value requires a profound reshaping of your day-to-day workflow and digitization of transactions and processes to generate and collect all useful data. A modern data management solution helps consolidate and cleanse data from all sources, transform it into reliable data, and provides relevant insights and recommended actions in the context of your operational applications using predictive analytics and machine learning. It allows you to apply analytics to improve the performance of your core operations. It doesn’t stop there and further correlates downstream business actions and results back in an integrated closed-loop, thus converting big data into smart data, providing faster Time To Analytics (TTA), measurable ROI, and better outcomes.
Modern Data Management Brings Together Best of Breed Technology in a Unified Platform
3. Take Polyglot Data Storage Approach to Achieve Big Data Scale as well as Performance
Different databases are designed to solve different business problems. Using a single database for all the requirements usually leads to non-performant solutions. A data-driven application should be able to bring together data from different database types to achieve the business objective. Most of the operational business applications run on relational, columnar databases, but they do not manage relationships well. Graph databases, while suited for uncovering and handling relationships, don’t have the horizontal scalability and agility to meet enterprise needs. This limitation is leading to the emergence of modern data management platforms built on columnar-graph hybrid stores. Once you create data-driven applications on a reliable data foundation of a Commercial Graph, you can visualize all relevant information and relationships as well as quickly pivot from one application to the other. For example, you can see all the purchased products and stores visited in a consumer’s profile, and with a simple click, you can drill into the product profile, roll-up dynamic hierarchical information (revenue, value, product usage) or find key influencers in customer networks.
4. Strike the Balance Between Offensive and Defensive Data Strategy
Retailers who fail to comply with data security can be fined up to 4% of their revenue and lose the confidence of their customers forever. On the other hand, personalization can deliver five to eight times the ROI on marketing spend, and can lift sales by 10% or more. Thus, the need of the hour is to strike a balance between your defensive and offensive data strategies and make considered trade-offs between “defensive” (e.g. security, governance, and compliance) and “offensive” (e.g. revenue growth, profitability, and customer satisfaction) uses of data as illustrated in this HBR article. A modern data management platform offers flexible data and information architectures that involve both a single source of truth (SSOT) and multiple versions of the truth (MVOTs). It focuses on reliable data at scale for “defense” and delivers relevant insights for “offense” from complete contextual 360-degree views, for personalized engagement.
Can you prove the ROI of your data management efforts? Are you able to conquer the gap between your analytical insights and operational execution? Can your business teams leverage reliable data and relevant insights to solve their day-to-day challenges without compromising on scalability and performance? Are you able to implement a mix of defensive as well as offensive data strategies to meet your data security as well as personalization goals? If the answer is no to one or more of these questions, you must rethink (and reinvent) your data management philosophy.
This week I got an opportunity to present at DBTA’s 2017 Data Summit conference. The topic of my discussion was “Turning the Customer Data into Actionable Insights.” All enterprises want to understand their customers better so they can engage the right customer, at the right time, with the right offer, via the customer’s preferred channel. The objective seems simple, but is quite hard to deliver if you do not have access to reliable data. Large volumes of data are being collected, but the data is scattered across multiple systems. There is no single source of truth across functional groups like sales, marketing and support. Different channels have their own version of the truth. Therefore, the customer experience remains disconnected, and customer insights are quite shallow.
The presentation covered how we can get to personalization at scale using Modern Data Management. The following aspects were covered:
Establishing a Reliable Data Foundation
To Make this experience more connected, we must bring the customer data together and then use that data for meaningful consumer insights and intelligent recommendations.
Start with connecting to all required data sources – internal systems (CRM/Marketing Automation etc.), external systems, social streams if needed as well, and enrich it with third-party data subscriptions as needed. Match, merge and clean the data to create a single, reliable source of truth of your customer profiles. Modern Data Management lets you identify potential matches and overlaps of the profiles. It helps to compare and contrast similar profiles and then automatically consolidate to create operational values using survivorship rules.
Please fill out the form below to download the presentation slides:
Uncovering and Understanding Relationships
The next important step is to reveal the relationships between the data entities. This where the graph technology helps us understand relationships – with a Commercial Graph (similar to LinkedIn or Facebook) you can relate customer profiles with products, accounts, family members and locations. You can establish many-to-many relationships between these data entities to understand where customers shop, what the products of interest are and who can influence their decisions. Uncovering relationships using graph technology helps you with identity resolution, finding influencers in the customer segment, or group individuals into a household and develop targeted campaigns. For B2B customers, you want to see the organizations and business units connected to it, key stakeholders and users of your products, or even contracts associated with various entities.
Single Source of Reliable Consumer Data for Operations and Analytics
Once you have the reliable data foundation, you can provision the data to all customer applications and channels for the connected experience. Moreover, you can provide the data to analytics systems to gain deeper insights about:
Relationships: Modern Data Management lets you utilize predictive analytics and machine learning to guide users and provide intelligent recommendations, based on data and behavior. It helps with identity resolution, can suggest your new relationships and identify influencers (like LinkedIn.)
Next-best-action: Recommendations like the next best offer to send to a customer, at the right time, using their preferred channel and identifying the key influencer to contact in an account and what to offer.
Data quality: Recommendations to improve data quality by suggesting better matching rules, finding potential matches as you onboard new data sources and determining profiles with poor data quality and wrong addresses.
With Reliable Data, Relevant Insights and Recommended Actions enabled by Modern Data Management, we can understand the customer better and provide more connected experiences.
Deliver personalized customer experiences with an agile, scalable and smart MDM.
Whether you’re a CIO in the Pharmaceutical Industry, Chief Customer Officer in Retail, or CMO in Healthcare, you’re trying to better understand your customers and deliver exceptional customer experiences at every touchpoint.
Innovative Global 2000 companies across industries are using Reltio Cloud to better understand their customers, personalize customer experiences, and accelerate their digital transformations.
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