It’s Cyber Monday Blues for Retail E-Commerce
Posted by Donald Soares on 30 November 2015 09:00 AM
E-commerce is set for significant growth this year. According to the Adobe Digital Index 2015 Holiday Shopping Predictions,1 2015 U.S. Holiday Season sales i.e., sales during November and December, will grow to an all-time high of $83 billion – an 11% year-over-year increase.
Meanwhile U.S. online sales on November 30 – Cyber Monday – are forecasted to see a 12% year-over-year increase, climbing to a record $3 billion in one day. Certainly nothing when compared to 11/11, or Singles’ Day2 in China this year, when $14.3 billion in goods was sold through Alibaba’s online sales platform – but not bad!
Yet, for most retailers e-commerce continues to face significant challenges and missed opportunities because the end game boils down to converting online visitors into buyers. As per industry analyst Monetate3 in Q4 2014, a mere 2.84% of online visitors to retail e-commerce websites actually bought anything. Also, analysis of traffic patterns showed that less than 1% of shoppers on mobile devices actually made a purchase. Even when online shoppers added items to their carts, two out of three – or 66% – did not end up completing the transaction.4
Worse, despite billion dollar investments by Wal-Mart and Target in building up their retail e-commerce infrastructure most U.S. consumers prefer Amazon.com as a shopping destination. A recent Reuters/Ipsos poll,5 found that 51% plan to do most of their online shopping at Amazon this holiday season, compared to 16% at Wal-Mart, 3% at Target and 2% at Macy’s. So, Amazon.com is not just winning the war online – but also managing to steal in-store sales from brick and mortar retailers as well. As per a November 18, New York Times article, “Amazon has built more than 100 warehouses from which to package and ship goods and it hasn’t really slowed its pace in establishing more.”6 Not to mention the success of its Prime Service that has garnered millions of subscribers with its unlimited “free” shipping at $99 per year.
At MarkLogic, we’ve identified three major pain points that are holding store-based and pure play e-commerce retailers back:
Finally, it’s all about the conversion – and the data integration. My colleagues put together this infographic and slide show that illuminate the pains of winning digital consumers and the benefits of using Enterprise NoSQL to quickly connect online customers with the information and solutions they need.
We’re hosting a webinar that will cover the challenges of personalization on December 2 at 11:00 EST. To sign up, click here. Don’t miss out on this opportunity to re-invent your business for true profits!
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A New Way to Master MDM
Posted by Christy Haragan on 25 November 2015 08:10 AM
Why can it be so hard to achieve? Something I’ve learned with problem solving is: if you find yourself spiraling into a pit of complexity, often the right approach is to go back to the start and look for another way.
For centuries, astronomers struggled to map the paths of the stars and the planets. The more they tried to map their models to the actual data, the more complex these models became, involving wild calculations that would make even the most hardened mathematician cry. That was, until Copernicus came along and suggested: What if the earth wasn’t the center of the Universe, and everything revolves around it? What if, instead, the Earth revolved around the Sun? And just like, that these absurd and complex models were reduced to a beautiful simplicity.
Of course, astronomy is still a difficult subject; but by finding the right start, vast amounts of unnecessary complexity were removed from the problem.
Data Sources As Astral Bodies
MDM projects, are essentially the same thing. Instead of astral bodies you have data sources. Instead of geocentricism (the earth at the center of the universe model), you have relational technology; whole IT budgets have been wasted trying to make it fit this problem. The question then is, what is the corresponding heliocentric (earth revolving around the Sun) model?
First a definition: MDM comprises the processes, governance, policies, standards and tools that consistently define and manage the critical data of an organization to provide a single point of reference.
For critical (master) data two criteria are required:
As you can imagine those two criteria are aspirational because organizations frequently will have a myriad of overlapping data, which arises from both organic growth, and immature data management.
To overcome this, organizations invest in huge squads of people who seek to consolidate, clean and de-duplicate their master data — and then manage this single true version of the truth going forward. They may wish to do this using a registry style (leave the data where it is, and instead maintain a registry of which data sits where), or a hub style (where they move the data into a single repository and manage it from there). In either case, because this is dealing with the most critical data to the business, a fully enterprise grade solution is required, with ACID transactions, HA, and DR to ensure data is always consistent, never lost, and always available.
There are, however, a number of challenges associated to the pursuit of this managed single version of the truth, which often results in very large multi-year multi-million dollar projects (in the best case), or outright failure (in the worst). Over two-thirds of all MDM projects fail!
Registry vs Hub
Registry approaches have the best chance of succeeding, but have the challenge that if the Christy Haragan entity is referenced (and perhaps duplicated) across multiple systems, the processes involved in maintaining a single version can become expensive and error prone (conflict resolution, for example). This approach works for smaller projects with smaller more localized data sets (e.g., spanning a single data center or perhaps geographic location), but as you can imagine doesn’t scale.
MDM Registry style: Data is left in the source systems and managed centrally.
The hub approach, does scale, but requires a domain model (e.g. Customer, Product, Account, etc.). Due to the inherent intricacy in any of these domains, these models are extremely large and complex. The process of mapping the source data onto these domain models is extremely challenging, expensive, and error prone. Moreover, as data is changed and re-arranged, (shape-shifting at its finest) the risk of breaking existing processes increases, and adds further risk and expense to the project.
MDM Hub style: Data is mapped to a domain model and moved to the central hub to be managed.
On top of those constraints, a common challenge to both of these approaches is the inherent problem of data cleansing and data de-duplication. A successful data cleansing exercise might automate 80 percent of records, but that can still leave you with quite a bit! A modest number of a million records to process (a low number in MDM terms), this still would require 200,000 records to be manually cleansed. And in the case of multiple addresses, how would the system know which is correct? Maybe both are, maybe neither.
Heliocentric Solution for MDM
There are, however, two approaches that can be taken to address these fundamental and challenging problems with a traditional MDM solution:
Of course most schema-agnostic approaches won’t provide a lot of the enterprise grade features of ACID, HA and DR.
MarkLogic provides a schema-agnostic database platform allowing data to be stored in its original form, and enriched as necessary. It’s semantics capability allows flexible links to be created between entities. But it has those highly-sought after enterprise features — so the business doesn’t sacrifice anything in adopting this new approach to managing it’s most critical data.
MDM, like Astronomy, is still a difficult topic. But by starting with the right approach, we can reduce large amounts of unnecessary complexity.
MarkLogic is MDM’s heliocentric solution.
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