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How to Partner Better in a Data-driven World

Ramon Chen, Chief Product Officer, Reltio

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.

This article in The Verge caught my attention:

MoviePass threatened with lawsuit after slashing subscription fees to $10 a month 
by Thuy Ong

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:

  1. Showtimes of movies (beyond opening week of new blockbusters) are rarely full, meaning there is unused inventory in every single time slot

  2. Pricing strategies to try and fill these slots don’t appear to have changed much beyond off peak time discounted ticket offers

  3. Loyalty and rewards programs have now started to become more prevalent so efforts are ongoing to capture consumer profiles

  4. 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:

  1. 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.

  2. 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

  3. 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

  4. 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

  5. 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:

  1. 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

  2. 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.

Challenges in Leveraging Big Data in Retail

Ankur Gupta, Sr. Product Marketing Manager, Reltio

1. Consolidate and cleanse data from various sources:

Retailers want to bring data together from multiple internal, third party subscriptions, public, and social sources to create a complete and accurate view of their customers. They want to uncover relationships, not just between consumers and products, but locations and family members as well to solve the householding issues. They want a single source of truth of customer data across functional areas and a reliable data foundation for accurate customer segmentation and identification of the influencers.

2. Gain relevant insights from omnichannel data:

There were several discussions around retailers wanting to blend interaction data from various channels with consumer profile information, giving marketing, e-commerce, and customer support teams visibility into customer preferences, product interests, and channel choice. Retailers want to deliver insights like churn propensity, lifetime-value, and abandonment rates to relevant teams in the context of their role and objectives. Furthermore, many leading retailers are leveraging machine learning and predictive analytics to suggest next-best-actions to send relevant and consistent information, across all channels, to the customer and find opportunities for up-sell and cross-sell. However, there is still a concern about the reliability and completeness of the data utilized to run such analytics.

3. Create a global product master:

Several retailers want to create a complete product or SKU profile to understand the supply chain relations, contract adherence, consumption per location, overall global business value and even social sentiments about their brands. They want a worldwide real-time view of the product, especially during a launch, to gain critical insights into accurate targeting and managing key influencers in the marketplace, designing relevant promotions and devising social media strategy.

4. Break data silos across departments:

Retailers are looking for ways to encourage collaboration across teams, in real time. With global multi-functional teams, multi-product portfolio, and big data scale consumer information, it is critical to allow as well as secure access to a convergence of information, with the proper level of role-based access and visibility. Data management has to be a shared responsibility across all functional groups and tools for social curation of internal data in the form of annotating, workflows, tagging, and voting allow every member to contribute and continuously improve data quality and the enterprise knowledge.

5. Exchange data with external parties:

There were some interesting discussions about the possibility to share the data externally with the suppliers to establish a single holistic view of the supply chain. Historically, most retailers do not have the infrastructure to process and make transaction-level data accessible easily. Fortunately, this technology is now available as Data as a Service (DaaS). Retailers can efficiently carve out a data view in the cloud and share it with partners or even monetize their data to create new revenue streams. The advantages of retail data sharing include improving on-shelf availability, better demand forecast accuracy, and improving the customer experience, among many others.

6. Be compliant:

With so many teams working with consumer data, retailers need comprehensive auditing and tracking features to guarantee compliance. They want a historical trail for any data merged or updated and want to get alerted to abnormal data viewing patterns by application users for possible information breach or theft. Compliance and transparency need to be inbuilt into the data management rather than treated as reports developed as an afterthought.

According to a McKinsey study, the continued adoption and development of big data levers have the potential to increase US retail productivity by more than 0.5 percent a year through 2020. Such a boost in profitability is especially significant in a sector where margins are notoriously tight.

Are you ready to address the above pain points and turn your big data into a valuable asset? Answer these seven questions to learn how prepared you are to manage your retail data effectively.

Why Life Sciences Must Go Beyond Master Data Management (MDM) …. Again … with Big Data Analytics

The annual CBI conference in Philadelphia will take place Nov 3-4 at the Wyndham. As in the previous 2 years the topics center around customer (HCP) master data. In fact, this year the conference has been renamed (yet again) from Commercial Data Congress (2013) to Master data Management (2014) to now Enterprise Customer Data Integration and Innovation (2015). It’s ironic because no one has really used the term Customer Data Integration (CDI) since 2005 when Gartner decided MDM was the better moniker.

For the first time Reltio will be participating at the CBI event and we look forward to shaking things up a bit with our participation in workshops, panels about open payments, and sponsorship of the overall event.

In addition to participating at CBI, the Reltio team will also be at the Innovation Enterprise Big Data Analytics in Pharma Summit across town on the same days Nov 3-4 at the Rittenhouse Hotel. That event will be all about the latest NoSQL databases, Hadoop and massive data sets, they won’t be focusing on master data, and that too will be a mistake. We believe that a MDM foundation is required for reliable data regardless of big data volumes, and it’s impossible to get relevant insights and recommended actions without it.

Why do we care so much about this topic?

Many of the team here at Reltio formed the nucleus of Siperian (acquired by Informatica in 2010), the leading on-premises MDM tool widely adopted by life sciences companies. Back in 2005, master data management  (MDM) was just taking shape and companies used MDM primarily to improve Siebel CRM data quality before upgrading and migrating their on-premises systems. Back then Siperian was preferred by many to the “seamlessly integrated Siebel Universal Customer Master (UCM)” offering, proving that best-of-breed solutions can be superior to integrated offerings that are designed for a single primary purpose.

One of the biggest issues we faced with Siperian (now Informatica MDM) was defining a relational life sciences data model that could capture not only the basic attributes of healthcare professionals and organizations, but represent real-world  HCP-to-HCP, HCO-to-HCO and HCP-HCO relationships. Also thrown in for good measure was an emerging need to master product data, product hierarchies, groups and baskets for pricing and competitive analysis, and to feed product information (PIM) systems.

At Siperian we admittedly struggled with basic hierarchy management and performance issues with merge and especially unmerge. While we preached multi-domain and coined the term Universal MDM, we were never completely successful with standalone product master data management, let alone bringing together both customer and product data into a single consolidated Siperian Hub.

Back then, the best databases we had to model and store life sciences entities and their relationships were the likes of Oracle, DB2 and SQL Server. Cloud and big data technologies such as graphs, columnar stores, HBase (on Hadoop) and Cassandra simply weren’t available.

Fast forward to the present, the MDM landscape remains more or less unchanged despite a quantum leap in technology.

  • Informatica MDM is still an on-premises solution with many of the same challenges we faced while at Siperian
  • Cloud-based customer master only offerings such as Veeva Network continue to focus on improving CRM data quality. Much like Siebel UCM did for Siebel CRM over 10 years ago
  • Customer and product masters are still supported through separate siloed hubs, even when built using the same tool. In fact, Gartner continues to publish separate customer and product magic quadrants as if to re-enforce this fact
  • Master data must still be delivered to data warehouses or operational data stores in order for business users to get a promised “complete view”
  • MDM systems and tools built on 1990s relational database technologies continue to hinder the ability to model real-world many-to-many-to-many relationships that graph technologies are designed for

For the most part, life sciences companies are no closer to getting basic affiliation management functionality, or their dream of an all encompassing key account management application as they are hindered by legacy MDM tools. Even a new wave of cloud-based MDM solutions do not make things any better. The good news is that life sciences companies can avoid a new kind of MDM (“Making Da-same Mistake”) … again.

The most popular consumer facing applications today such as LinkedIn and Facebook have shown that business facing data-driven applications can be cloud-based, handle multiple data domains, manage structured, unstructured, master, transactional, activity and social data. Companies should expect complete end-to-end modern data management delivered as Platform as a Service (PaaS) instead of relying on recurring “next generation master data management” promises that remain unfulfilled.

I’ll be attending both events because like everyone here at Reltio I’ve got the DNA and passion in me for both. I look forward to seeing you at either … or both events. In the meantime, here are some additional musings around the future of MDM. Please let me know your thoughts.

Show Me (and let me Act on) the Data! The Days of Master Data Only Reports are Numbered

For over 10 years master data has been locked away in MDM tools with limited visibility of the profile and state of the data. Even the data stewards, tasked with the manual resolution through workflow queues have had poor feedback from traditional tools as to the state of their most important asset. MDM vendors’ general response to this challenge has been either to:

  1. Expose the hub metadata through a published model and to allow customers to “roll their own insights” via BI tools such as Microstrategy and Business Objects

  2. Leave it to the users to export the data out to a separate data mart or warehouse, or worse excel spreadsheets for review

Typically the lag and latency associated with analyzing the data quality is of concern, since an agile organization has data continuously coming into the system. Like the painting of the Golden Gate bridge, the work is never done. When you reach one end point, you just turn around and start again. 

Furthermore, narrowing efforts to focus on the data that needs the most attention has always been a challenge. There is only so much manual effort that can be applied as resources and expertise are limited.

Being able to quickly filter and see data issues, or have the system provide the alerts and recommended actions is a capability that is available in today’s modern data management Platform as a Service (PaaS). And since all of this should be running in a multi-tenant cloud, using a browser-based and mobile interface, any filtering and creation of lists of data can easily be stored as a URL and shared with colleagues for collaboration.

While traditional MDM vendors are trying to incorporate this level of functionality and basic reporting, most companies have moved on.

 

Companies expect not only in line insights and reporting directly from within the tool, they now want access for frontline business users. Not just so they can see the quality of data for themselves, but to allow them to make changes, offer suggestions, and comments at the point of engagement. 

These capabilities can only be found in a new generation of data-driven applications. They provide complete transparency as to the quality of the data, feature complete social collaboration and curation functionality at scale, as well as allowing third party data to augment and replenish gaps in quality in real-time via Data as a Service (DaaS). In other words, reporting is nice, but action speaks louder than reports.

Another reason why reporting on just master data is limiting, is the increased demand to have not just customer entities, but product, organization and other entities and affiliations to provide the big picture. Additionally all related transaction and interaction data are required to be more closely tied into profiles, hence the continuing requirement to create data marts and warehouses.

So merely improving the reporting capabilities of the MDM tool is necessary but not sufficient. Frontline business users want operational execution and the ability to immediately correct or provide input about the data from the same interface they are using for their daily operations, not just the ability to retroactively get reports about the data. Better still, the system should have the smarts to provide recommended actions in the context of workflow to guide both business users and data stewards as to what to do next. Or to take action on their behalf.

With all the amazing modern data management technology available, your company should expect and get more by going beyond just MDM, by providing your business teams with fully integrated data-driven applications. The next time someone offers to “Show you the data!”, ask them whether you can do something about it together as a team.

Cognizant and Reltio Webinar Recap and Replay

Click here to listen to the webinar replay

Master data management (MDM) as a discipline and a technology has had its ups and downs over the last 10 years. With significant investment in multi-million dollar projects, many enterprises certainly have the right to expect a more direct impact on the business.

With the explosion of data volumes and the wide adoption of cloud and mobile technologies, frontline business users are starting to expect a new breed of data-driven applications. They are comparing what they have today with the ease-of-use and agility of consumer applications such as LinkedIn and Facebook. 

This webinar replay shows how enterprise data-driven applications backed by a modern data management Platform as a Service is breathing new life into MDM, and allowing both IT and businesses to be more agile and effective. It delves into specific industry use cases from Life Sciences, High Tech, Information and Entertainment to illustrate how organizations in these industries are leap frogging competitors by using data as a strategic asset.

Joining the Disrupting Digital Business Movement

Two months ago, R “Ray” Wang, Principal Analyst, Founder and Chairman of Constellation Research, published “Disrupting Digital Business” (DDB). At the end of this blog post, we will be offering you the opportunity to win a signed copy of Ray’s book, so please read-on.

It immediately received universal praise from technology and business leaders alike. 

Ray Wang assembles the strands of digital DNA that are key to building the successful companies and leaders of tomorrow

— Mark Benioff, Chairman and CEO, Salesforce.com

Indeed, this book has it all. Written in an engaging style, with examples and anecdotes that make it easy to relate to and remember.

Being fortunate enough to have received an advanced digital copy two months prior to launch, I literally devoured it from start of PDF page 1 to closing acknowledgements in under two hours.

So why wait four months to write this review? At first I was shocked that I was able to consume the information in the book in less than the time it takes to complete a baseball game. Rather than re-read it again, I decided that the true test of the book would be how much I was able to apply the concepts, and write this review once time had past, without picking the book up again a second time.

So here goes:

  • Ray starts off by emphasizing “brand promise” as core to a disruptive digital business
  • Cites Disney and Marriott as examples of companies that do that extremely well
  • Points out that organizational DNA, not just technology is critical
  • Mentions Sony’s double walkman (actually sat on their own MP3 technology because they were worried about cannibalizing their own music sales) as an example of incremental innovation vs. Apple iPod as transformational innovation. (Consumers paying 99 times more than free, because of the great iTunes customer experience)
  • Explores contextual relationships and the digital exhaust (vast amounts of data that allow businesses to understand their customers). Especially relevant to us at Reltio since we have delivered data-driven applications (DDA) that help companies better manage their key accounts, containing complex network of people and organizations
  • Covers what he calls the “intention driven mindset”, aka predictive analytics, relevant insights and recommended actions through machine learning. Again relevant to us as core capabilities of Reltio Cloud
  • Brings to life the notion of a Peer-to-peer economy and markets, something we believe in, and have enabled in the context of data sharing through data-as-a-service
  • Summarizes what it takes to be a disrupting business, including using new technology that plays well with existing legacy systems and processes
  • And finally the move to data-driven decisions from gut decisions. He references the failures of data warehouses and marts, and even the attempts to use master data management in an effort to solve the problem. Emphasizing that billions have been spent in a repeating technology cycle, with no real success

No doubt I missed quite a few key things in the book through this attempt at four month recap. The fact that I was able to list out some nuggets is a testament to how well the book was written, and how much we at Reltio believe in Ray’s perspectives. 

Our vision at Reltio aligns with Ray’s and we believe data-driven applications (DDA), with a modern data management foundation, are needed to join the “Disrupting Digital Business” (DDB) movement. At Reltio, we have begun practicing many of Ray’s concepts, and we use the Reltio Cloud to run our own business. You might say we’re going from DDA to DDB.

To win a signed copy of Ray’s book, please like or share this post using the icon below.