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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.

Is it About the Groceries or the Data? How Whole Foods Tried to Avoid Being Amazoned

Ramon Chen, Chief Product Officer, Reltio

Amazon announced that it is acquiring Whole Foods for $13.7B.  There are several theories going around about the move, from direct competition with Walmart, to an interesting theory by Blockchain Expert Richie Etwaru, who ties in Amazon’s designs on becoming a pharmacy with the deal, and whether they might go after a healthcare payer next. Some even are speculating that Nordstrom is next on the Amazon shopping list.

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.

Based on what we know about customer behavior, attitudes we know about that store and region, we want to make decisions on everything from assortment planning to space allocation, pricing, promotion. A lot of that has got to do with insights from the platform and having better analytics to make decisions.

 

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.

We’re OK with this decision, for two reasons. One, the ‘chinese wall’ — the commercial commitment that Amazon does not have access to the data or any of the things that are really being processed within AWS within this solution. We’ve had specific direct conversations with Amazon involving our legal teams and really understanding that limitation.”

If Amazon were ever to breach those commitments I think it would be devastating to their business — and a quite profitable business for them as well.

There’s also the huge consideration that this is the largest, best and most cost-effective platform in this space. Another partner would not yield as good a result. It is not just cloud hosting, it is a platform, a toolset, that allows speed-to-market that are not offered by other providers.

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.

Other data experts saw this coming as well. Bill Schmarzo, CTO, Dell EMC Services (aka “Dean of Big Data”) posted an amazingly relevant digital transformation blog post, with a Grocery chain case study less than a week ago!

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.

At Reltio, we’ve honed our Modern Data Management Platform as a Service (PaaS) to give companies the agility they need to not just survive, but thrive. Incidentally, Ajay Khanna, Reltio’s VP of Marketing will be presenting on this exact topic at the MITCDOIQ Symposium in Cambridge, MA on July 13th. We hope to see you there.

Turning Customer Data into Actionable Insights

Ajay Khanna, VP of Marketing, Reltio

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:

  1. 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.)

  2. 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.

  3. 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.