Fortune 500 companies are making large investments around Programmatic Marketing (“Marketing that learns”). One of most often implemented use case in Programmatic Marketing is customer journey mapping and analytics.
Why? Because, deciphering the nuts-and-bolts” of individual customer journeys online (and deducing intent) is core to improving customer experience and driving brand loyalty.
Specifically, the objectives are:
- Visualize and map the end-to-end customer journey by personas
- Optimizing on the right journey attributes to increase yields by >30% lift… Uncover the right combination of web, mobile and physical channels, content and experiences that best achieves the target goals
- Enable marketers to identify journey bottlenecks for individuals and aggregates
- Leverage actual behavior data to enhance and personalize the experience for each individual customer
More data + Better models + More accurate metrics + Better approaches & architectures = Lots of room for improvement!
There are clearly massive foundational shifts taking place around big data. I am not sure how large conventional Fortune 500 firms can innovate and keep up with what’s going on. I have run into CIOs who have not heard of Hadoop in some cases.
It’s also fascinating to see how data-driven “bleeding” edge firms like NetFlix are pushing the envelope. Netflix stats are amazing: 1/3+ Internet traffic (NA / peak); 100+ Million hours per day; 65+ Million members / 50+ countries; 500 Billion Events / Day.
NetFlix is clearly reinventing Television and targeting 90 million potential subs in the US market alone. Binge-watching, cord-cutting are now part of our everyday lingo. What most people don’t realize is how data-driven Netflix is…. from “giving viewers what they want” to “leveraging data mining to boost subscriber base”.
Viewing -> Improved Personalization -> Better Experience is the virtuous circle.
Here is a glimpse at how their BI landscape has evolved in the past five years as they integrate 5 million to 6 million net adds for several years now. The figures are from a presentation by Blake Irvine, Manager Data Science and Engineering.
BI tools @ NetFlix pre-Hadoop
New Technologies | New Possibilities
As a C-level executive, it’s becoming clear to me that NoSQL databases and Machine Learning toolsets like Spark are going to play an increasingly big role in data-driven business models, low-latency architecture & rapid application development (projects that can be done in 8-12 weeks not years).
The best practice firms are making this technology shift as decreasing storage costs have led to an explosion of big data. Commodity cluster software, like Hadoop, has made it 10-20x cheaper to store large datasets.
After spending two days at the leading NoSQL provider MongoDB World event in NYC, I was pleasantly surprised to see the amount of innovation and size of user community around document centric databases like MongoDB.
Data Driven Insight Economy
It doesn’t take genius to realize that data driven business models, high volume data feeds, mobile first customer engagement, and cloud are creating new distributed database requirements. Today’s modern online and mobile applications need continuous availability, cost effective scalability and high-speed analytics to deliver an engaging customer experience.
We know instinctively that there is value in all the data being captured in the world around out…no question is no longer “if there is value” but “how to extract that value and apply it to the business to make a difference”.
Legacy relational databases fail to meet the requirements of digital and online applications for the following reasons:
“Have we got a girl for you” Some very sophisticated predictive analytics are powering the online dating or hookup world. A lot of innovation is taking place around real-time, geo-location based matching services.
Take for Match.com which debuted its online dating first site in the U.S. in April 1995. Today, the Match.com brand hosts sites in 24 countries, in fifteen different languages spanning five continents. Match.com offers an interactive way for singles to meet other singles with whom they might otherwise never cross paths.
How to model and predict human attraction? Match.com is powered by Synapse algorithm. Synapse learns about its users in ways similar to sites like Amazon, Neflix, and Pandora to recommend new products, movies, or songs based on a user’s preferences.
Match.com uses Chemistry.com to do personalized surveys and get detailed preference data. But when it comes to matching people based on their potential love and mutual attraction, however, analytics get significantly more complex when you are attempting to predict mutual match… the person A is a potential match for person B…. but with high probability that person B is also interested in person A. Read more
Technology (preventative apps like Apple Health and HealthKit; EHR, claims and reimbursement analytics; Physician Practice management etc.) will reinvent healthcare as we know it. I expect the healthcare transformation to start incrementally and develop slowly in sophistication. Though the early changes will appear clumsy and underwhelming, by 2030 they will seem obvious, inevitable and well beyond the changes we might envision today.
Why change? Consider this:
- Honeywell, a Fortune 100 technology and manufacturing company, needed to manage the ever-escalating cost of insuring its 130,000 employees and their dependents. Honeywell has reported that health care costs were growing approximately 8-10% per year.
- Self-insured employers like Wal-Mart want to make health care cost and quality information available to their 1.2 Million employees. Useful information that can be used by employees to select physicians based on how their rank, or how much they cost, resulting in savings for both the employee and the employer. Decision support enabler.
- IBM is moving to a private health exchange…Extend Health private exchange will be handling plan options for 110,000 IBM retirees
- Walgreens is moving employees to a Corporate Health Exchange. Of the 180,000 Walgreen employees eligible for healthcare insurance, 120,000 opted for coverage for themselves and 40,000 family members. Another 60,000 employees, many of them working part-time, were not eligible for health insurance.
- Trader Joe’s — decided to send some employees to the new public exchanges. Trader Joe’s has left coverage for three-quarters of its work force untouched but is giving part-time workers a contribution of $500 to buy policies. Because of the employees’ low incomes, the company says it believes many will be eligible for federal subsidies to help them afford coverage.
For the past year I have done strategy and implementation work in the employee Healthcare benefits and Private Exchange area. I wanted to share my insights into the massive structural changes taking place in health insurance. The move to patient-centered, consumer-driven, and value-based models is real.
This posting has been updated and posted on disruptivedigital.wordpress.com
A satisfying experience is the driver of any business’s revenue growth. Disney Theme Parks is no exception. Disney is executing a guest (and fan) personalization strategy leveraging wearables (and analytics) to track, measure and improve the overall park experience. The goal is increase sales, return visits, word of mouth recommendations, loyalty and brand engagement across channels, activities, and time.
Wearables are the next big thing. The new crop of gadgets — mostly worn on the wrist or as eyewear — will become a “fifth screen,” after TVs, PCs, smartphones, and tablets.
Wearables are already being used to monitoring vital signs, wellness and health. Devices like Fitbit, UP, Fuelband, Gear2 track activity, sleep quality, steps taken during the day. Consumers of all sorts — fitness buffs, dieters, and the elderly — have come to rely on them to capture and aggregate biometric data.
What most people don’t understand is how powerful wearables (coupled with analytics) can be in designing new user experiences. Businesses thrive when they engage customers by creating a longitudinal predictive view of each customer’s behavior. To understand the wearables use cases and potential we did a deep dive into a real-world application at Disney Theme Parks.