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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.

Three Critical Ingredients for AI, Machine Learning & Cognitive Computing Success

Ramon Chen, Chief Product Officer, Reltio

You may already know a lot about Artificial Intelligence (AI), Machine Learning (ML), Deep Learning or even what some vendors call Cognitive Computing. Or maybe you are still trying to understand the nuances and differences between each term, and how they relate to each other.

Either way, it’s easy to be seduced by the “black magic” of technology that can solve a variety of your business challenges by just asking Watson, Einstein, Siri, Alexa, Hal (click for the iconic HAL 9000 scene in Space Odyssey) or other “humanizing” names.

In fact Gartner’s 2016 Emerging Technologies Hype Cycle has Machine Learning at the very “Peak of Inflated Expectations.” Those who are familiar with the cycle, know what is likely to come next <Trough of Disillusionment cough cough>. 

 

If you want to protect yourself from the hype, here are 3 critical ingredients for your consideration:

At Reltio we’ve been articulating a vision, which includes a pragmatic perspective of machine learning (ML) for over 3 years now. Realizing that not only does ML not offer a silver bullet, but there is still much to learn (pun intended) as to how such technologies can ultimately benefit both IT and business. Noted Big Data expert Bernard Marr provides a nice list of use cases that might be applied to your specific business and industry. The key is that a focused set of benefits for each users’ role, must be defined in order for it to be accurately measured so it doesn’t get labelled yet another (data) science project with limited value.

#1 Create a Reliable Data Foundation

Most companies are NOT ready for any form of AI, ML, or Cognitive Computing to help their business user, because their data is such poor shape to even attempt such an endeavor. Ironically, a great IT use case is to use ML to first help improve the consistency, accuracy and manageability for better data quality (DQ), uncovering patterns, anomaly detection and assisting humans, such as data stewards, to make their job more focused and efficient.

#2 Bring Analytics and Machine Learning to the Data

Just as the process of aggregating data to perform historical or predictive analytics is a cumbersome and expensive process, gathering and blending all of the right data that will guarantee machine learning is effective must be the in the DNA of any Modern Data Management Platform as a Service (PaaS).  

Bolting on AI or ML into legacy master data management (MDM) systems, or using such MDM tools to feed downstream disparate ML tools is putting lipstick on hosted managed services disguised as cloud. Reliable data, relevant insights and recommended actions via machine learning needs to be seamlessly combined into one, single multi-tenant cloud platform, architected from the ground up, for both analytical intelligence and operational execution, through data-driven applications.

Successful execution requires a closed-loop of all data, insights and actions, to ensure accurate metrication for continuously improved outcomes. Further, a multi-tenant cloud environment is the only way sufficient storage and processing capacity can be elastically accessed on demand to meet any business need.

Another benefit of a multi-tenant cloud PaaS is the potential to use a wide variety of anonymized data to help with machine learning across all industries. Having a large enough set of data is a critical factor for smaller companies to benefit from the right recommended actions, for common industry use cases.

#3 Don’t Go all in on one vendor

In a rush to market that “our tools do it too,” large vendors will unfortunately, over promise, and under deliver. It’s not their fault, as they must respond to the market, but many face an unenviable task of achieving ingredient #2 above, let alone attempting to now also execute on a plan to deliver their own AI technologies. 

An open ecosystem that allows you to choose and partner with the technologies, and domain experts of your choice is critical to getting the most out of a still young and evolving landscape. Most companies are already trying to evolve out of their legacy MDM platforms. Getting further locked into a single vendor, delivering both MDM and ML, through siloed disparate tools will not provide “clarity,” and may further complicate an already fragmented data management strategy.

At Reltio, we formed strategic partnerships with companies like QuintilesIMS to combine our strengths to jointly deliver on a vision for the next generation MDM and analytics capabilities for life sciences.

In summary, look to master your data in #1 for a reliable data foundation. #2 ensure that it covers all data types, sources and modes of consumption in a seamless feedback loop on a Modern Data Management platform architected from the ground up to avoid further siloing your data. Finally, #3 give yourself the openness and flexibility of your partners of choice to meet your business needs. 

You don’t want a HAL-like failure that prevents you from realizing your true goal of improving your business.

Driving Digital Transformation Through Modern Master Data Management

Ankur Gupta, Sr. Product Marketing Manager, Reltio

Digital Strategy is not a separate strategy, but instead a new lens on the overall business strategy that incorporates customer intelligence, sales & service optimization and digital marketing efforts (e.g. mobile, e-commerce and social). Key drivers of digital transformation among B2C organizations are profitability, customer satisfaction and increased speed to market. Whether you are a retailer, restaurant, travel company, media & entertainment company, lifestyle brand, or a consumer bank, you want to create a seamless, personalized and consistent customer experience across all channels (web, email, phone, stores) and departments (marketing, sales, support).

However, personalization is a double-edged sword. All digital transformation and personalization efforts would fail if data underneath is of poor quality, siloed and delayed. According to an estimate, poor customer experiences result in an estimated $83 billion loss by U.S. enterprises each year because of defections and abandoned purchases. On the contrary, if done correctly, it can considerably boost your business (Amazon’s conversion to sales of on-site recommendations could be as high as 60%). Some of the top of mind issues discussed during various sessions included:

Garbage in – garbage out. 

Today, hyper-personalization is possible because of digital transformation enabled by cloud, mobile, big data, internet of things and many more cutting-edge technologies. Similarly, companies have invented great processes (order-to-cash, supply chain management, onboarding, provisioning) to manage customers, prospects, suppliers and partners. What’s still not treated as a top priority is the quality of data driving these processes.

Omnichannel initiative of any consumer brand is as good as its underlying data. A reliable data foundation alone, created through a Modern Data Management Platform, can lead to a truly personalized engagement,” said Ramon Chen, Chief Marketing Officer of Reltio, during the panel discussion, “Lessons from Lifestyle Brands: Omnichannel Marketing and Strategy.

The thought was echoed by other participants who discussed how reliable data is the key to effective customer experiences.

Data silos within the organization.

Being able to understand and monitor end-to-end consumer journey is vital. Yet, it is not uncommon for each team or function within an organization to define and measure customer experiences independently, in isolation. The result is disconnected global operations, fragmented multichannel visibility and incomplete views of customers that slow down all digital transformation efforts. Data-driven applications built on a Modern Data Management platform gives you not only the 360-degree view of your customers, products, channel partners, or suppliers but also a contextualized view of relationships among these entities. More importantly, it helps you create a single-source of truth across sales, marketing and support to provide the right customer with the right offering at the right time, thus enhancing the customer experience.

Data silos across organizations.

Co-innovation, co-creation and collaborative curation of consumer information, at a big data scale, can open the door to new possibilities. For example, personalized recommendations for food, movies, hotels and shopping can be packaged together for a particular consumer if the companies in these different industries are willing to share data with each other. Furthermore, such data exchange helps these non-competing business entities to monetize their precious consumer data assets. As digital touchpoints proliferation continues, a Modern Data Management solution will help companies to trade reliable data currency in a secure fashion.

Business outcomes don’t feed the underlying master data.

There are no mistakes, only lessons. It is critical to get smarter with each interaction. A Modern Data Management Platform coupled with Machine Learning enables contextual information and helps you 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. On top of that, a Modern Data Management solution captures all actual business outcomes and enriches the master data in a closed-loop, thus continuously fueling the recommendation engine for making smarter predictions.

It’s time for data to be managed in such a way to make true digital transformation happen. Once Modern Data Management is in place, a consumer brand can reap substantial benefits on a continual basis. Starting with proper data quality and alignment of key data assets across systems, departments and channels, it helps build a reliable data foundation for all your digital transformation initiatives. Businesses who will timely address this challenge will be ahead of the competition.

Accelerating Mergers & Acquisitions in the Medical Device Industry & the Rise of Data Monetization

As part of a very busy month for Reltio, I attended and presented at an event organized by Q1 Productions dedicated to Medical Device Industry Corporate Strategy and M&A in Atlanta, GA. The meeting was very well attended including representatives from Medtronic, Stryker and other companies sharing their insights into techniques for more effective M&A.

The topic of my presentation was:

Accelerating M&A in the Medical Device Industry with Modern Data Management

We discussed how bringing together clean, reliable, relevant data in a timely fashion from the IT systems of both parties to support an M&A is still largely a manual, inefficient and potentially inaccurate effort.

The audience agreed that the complexity of data siloed across both companies made it very difficult to analyze information within the legal and time constraints of all pending transactions. If the merger goes through, all work is generally discarded, leaving the combined company to start from ground zero. If the merger is called off, someone is left with hardware procured to support the M&A that is wasted. 

As part of the presentation I detailed:

  • How a multi-billion dollar merger blended & analyzed data from hundreds of sources with full security & audit controls, without spending a dime on hardware
  • Why they can also now realize millions of dollars in increased negotiating leverage through better vendor/supplier management
  • Which groups are positioned to benefit from new data-driven applications that will significantly improve the efficiency of their day-to-day operations
  • I also provided a modern data management Platform as a Service (PaaS) 101 overview, so attendees could understand the difference between MDM, Big Data, IoT, Analytics, Graph databases and Machine Learning
  • What other opportunities, including data monetization, are now possibilities for the future

Due to a cancellation I also was asked to dive deeper in a separate presentation on the topic of Data Monetization.

Data Monetization, Chief Data Officers, and Industry Clouds

This session provided details on: 

  • What exactly is data monetization
  • Why Data as a Service (DaaS) is a prerequisite
  • How reliable data with full compliance and audit controls are needed as a foundation
  • What are the legal ramifications?
  • Who is responsible for thinking about monetization?
  • What is a chief data officer, and what is his/her role?
  • How does a CDO play well with a CIO?
  • What are industry clouds, and how will that change the landscape of industry specific data applications
  • And even a topic what the future holds for our children’s future career choices: whether to learn how to program or be a data scientist

Finally the next day I moderated a lively “war stores” panel on

Lessons Learned from Recent M&A Activities

Distinguished panelists included

  • Girish Gangadharan, Analyst, Corporate Strategy & Development, ANALOGIC
  • Richard B. Smith, Partner, MCDERMOTT WILL & EMERY

We discussed how over the course of the past 18 months, the medical device industry has experienced some of the highest levels of mergers & acquisition activity recorded to date, and as a result, many executives and organizations have sharpened acquisition skill-sets and have also learned substantial lessons. A lively debate ensued with the panelists and audience around recent experiences, lessons, as well as challenges that lie ahead for the industry as it continues to evolve and grow. The discussion continued throughout the day as attendees identified best practices across the business development spectrum; from identifying appropriate targets to negotiating in order to secure the optimal outcome.

Other key points raised included:

  • Managing expectations for earn outs as part of the deal
  • The difference between acquiring pre-commercial and commercial companies
  • Bringing together sales teams, and managing cost synergies
  • Retaining founders of start-ups and incentives
  • Legal and contractual requirements

Medical Device M&A continues to be hot. A recent infographic by Medical Device Trends shows some of the latest statistics: 
(BTW their site is a wealth of information and you should follow them @mdtrends on twitter) 

Contact me if you’d like to chat about your experiences in life sciences and medical device M&A, or if you’d like a copy of my presentations.