Friday Oct 26, 2012

Analytics in an Omni-Channel World

Retail has been around ever since mankind started bartering.  The earliest transactions were very specific to the individuals buying and selling, then someone had the bright idea to open a store.  Those transactions were a little more generic, but the store owner still knew his customers and what they wanted.  As the chains rolled out, customer intimacy was sacrificed for scale, and retailers began to rely on segments and clusters.  But thanks to the widespread availability of data and the technology to convert said data into information, retailers are getting back to details.

The retail industry is following a maturity model for analytics that is has progressed through five stages, each delivering more value than the previous.

Store Analytics

Brick-and-mortar retailers (and pure-play catalogers as well) that collect anonymous basket-level data are able to get some sense of demand to help with allocation decisions.  Promotions and foot-traffic can be measured to understand marketing effectiveness and perhaps focus groups can help test ideas.  But decisions are influenced by the majority, using faceless customer segments and aggregated industry data points.  Loyalty programs help a little, but in many cases the cost outweighs the benefits.

Web Analytics

The Web made it much easier to collect data on specific, yet still anonymous consumers using cookies to track visits. Clickstreams and product searches are analyzed to understand the purchase journey, gauge demand, and better understand up-selling opportunities.  Personalization begins to allow retailers target market consumers with recommendations.

Cross-Channel Analytics

This phase is a minor one, but where most retailers probably sit today.  They are able to use information from one channel to bolster activities in another. However, there are technical challenges combining data silos so its not an easy task.  But for those retailers that are able to perform analytics on both sources of data, the pay-off is pretty nice.  Revenue per customer begins to go up as customers have a better brand experience.

Mobile & Social Analytics

Big data technologies are enabling a 360-degree view of the customer by incorporating psychographic data from social sites alongside traditional demographic data.  Retailers can track individual preferences, opinions, hobbies, etc. in order to understand a consumer's motivations.  Using mobile devices, consumers can interact with brands anywhere, anytime, accessing deep product information and reviews.  Mobile, combined with a loyalty program, presents an opportunity to put shopping into geographic context, understanding paths to the store, patterns within the store, and be an always-on advertising conduit.

Omni-Channel Analytics

All this data along with the proper technology represents a new paradigm in which the clock is turned back and retail becomes very personal once again.  Rich, individualized data better illuminates demand, allows for highly localized assortments, and helps tailor up-selling.  Interactions with all channels help build an accurate profile of each consumer, and allows retailers to tailor the retail experience to meet the heightened expectations of today's sophisticated shopper.  And of course this culminates in greater customer satisfaction and business profitability.

Sunday Feb 19, 2012

Shopping Habits

I recently read an excellent article from the NYTimes called How Companies Learn Your Secrets in which the author describes how retailers try to understand and shape our shopping habits.  Its a rather long article, so I'll do a bit of summation.

Recall when you first learned to drive how much concentration was required to back out of the driveway.  But now it's a fairly simple task that takes little thought.  That's because the brain has been taught this task, and it's very repeatable without expending tons of effort.  In other words, it's become a habit.  Habits are composed of three steps: cue, routine, and reward.  A large portion of the shopping we do is habitual, like grocery shopping.  There's very little complex decision making, and much of the in-store marketing is ignored.  Enter the toothpaste aisle, snag your brand, and check it off the list.  So how is a retailer to grab a shopper's attention to break out of the habit loop?

One trick is to identify "teachable moments" when a shopper is out of their routine and susceptible to influence.  It turns out that Target is very good at this.  They analyze their customer data to determine when events such as a new job, graduation, home purchase, and marriage have occurred and then do target marketing (pun certainly intended).  After all, those life-changing events can extend change to shopping habits, which will pay off handsomely over time.

Of course the big kahuna of life-changing events is the birth of a baby.  That information is available from public records, so many retailers use the opportunity to mail lots of diaper and formula coupons to new mothers.  If they can establish a relationship with Mom first, they have a better chance of retaining her and her family for a long time.  So to beat the competition, Target wants to market during the second trimester before the other retailers pile on.  But how the heck can that be done?

Diapers and formula are dead give-aways that there's a newborn, so work backwards and examine the products purchased by women leading up to the big event.  It turns out they buy lots of lotions and start switching to scent-free versions of detergent and soaps.  They buy vitamin supplements, cotton balls, and nursery furniture.  Target has gotten so good at their pregnancy prediction scores that they can often determine the due date and sex of the yet-to-arrive baby.

The article goes on to relate a story about an angry father walking into a Target store demanding to see the manager.  He was upset that Target was sending his teenage daughter coupons for baby supplies.  The manager apologized, and followed up a few days later to apologize once again.  However, it was the father that ended up apologizing because his daughter was in fact pregnant.  Oops.

As you can see, this has the potential to be a public relations nightmare, so Target wisely mixes in other coupons alongside the baby products.  This hides the fact from pregnant women that they're being targeted, and doesn't raise alarms with the boyfriends and husbands that are still in the dark.

Oracle plans to release the second module of our Retail Analytics family this year.  Its called Oracle Retail Customer Analytics.  'Nuff said.

Tuesday Oct 18, 2011

Moneyball for Retail

Back in 2003, Michael Lewis wrote Moneyball:The Art of Winning an Unfair Game which was also released last month as a feature film staring Brad Pitt.  The story focuses on the Oakland A's baseball team modernizing its scouting methods to be less subjective and more analytical, allowing it to compete with better funded teams.

Just like it’s not fair that the Oakland A’s $41M budget had to compete with the Yankee’s $125M budget, many retailers find it difficult to compete against Walmart, Target, and Amazon, companies that spend an enormous amount on IT. But it’s possible to follow Oakland’s lead and compete on analytics.  Retailers that better understand their customers will have an advantage, sometimes regardless of the prices they charge or the products they carry (although you really need all three to be successful over the long-haul).

Aileen Lee of Kleiner Perkins Caufield & Byers presented on this topic at the Web 2.0 Summit, which was covered by Richard MacManus in this article.  (Read the article for some real-world retail examples.)  Even small retailers can gain a competitive advantage if they (1) collect the right data, (2) analyze it correctly, and (3) act upon it quickly.

Traditionally retailers didn't do this type of analysis because the data just wasn't available, but since the advent of e-commerce much more data has been collected.  Other sources include loyalty programs, and more recently, social networks.  Retailers have optimized supply chains, new store locations, and even pricing, but the next generation of analytics will focus on individual consumers, understanding what they want and what offers will influence their purchase.

There are lots of ways to attack the problem, and one that's extremely scalable leverages Oracle's engineered systems as described by Jean-Pierre Dijcks:

Not every retailer needs this much analytical horsepower, but imagine if answers were available at the speed of thought.  Privacy aside, the possibilities to personalize the shopping experience are tremendous.

Wednesday May 25, 2011

Oracle Retail Merchandising Analytics Released

Today Oracle Retail announced availability of a new product called Oracle Retail Merchandising Analytics, the first of several BI applications planned for the retail industry. To further describe the product, I've asked Mark Lawrence, the brains behind ORMA, to explain the strategy and why this approach is different than what came before.


It's probably safe to say that those reading this blog are all too aware of retail's "data rich but information poor" reputation, and that today's competitive pressures are forcing the industry to compete on analytics. You can't improve on something if you don't measure it and monitor it, right?

After spending many years building a homegrown Enterprise Data Warehouse (EDW) at Circuit City (eh-hem, great BI was unfortunately not enough to save the company), I was hired by Oracle to lead the creation of a next-generation BI solution for retail. One that would leverage the full Oracle BI/DW technology stack, storage-to-scorecard, yet not necessarily require that full stack. One that would be optimized for Oracle's retail apps, but designed to integrate with non-Oracle data sources as well. One that would not only address retail enterprise needs, but those of the full corporate enterprise. One that was modularized so that it could serve as a retailer's EDW or that could augment an existing EDW with one or more specialized data marts, perhaps enabling a next-gen EDW via incremental data mart implementations. One that could surface BI, properly-filtered, to the right people, at the right time, using the right delivery method whether it be mobile, dashboards, or objects embedded in a planning or operational app. One that I would have wanted to employ at Circuit City, had it been available then (reminds me of my former dream of the "BI guy" saving the company and retiring early on stock options...).

So, Oracle Retail Analytics, with the first of five planned modules just launched last month, embodies all of those things. That first module, Oracle Retail Merchandising Analytics (ORMA,) is now Generally Available, is built on Oracle database 11gR2 and includes packaged integration using Oracle Data Integrator 11g with Oracle's merchandising product family, expansive Oracle BI 11g metadata and reporting, and a data model that is based on Oracle BI Applications 11g to enable cross-domain, retail + ERP/CRM analytics.

Each module is "plug-and-play" in that it includes packaged integration with the associated Oracle Retail applications, fully physicalized data model, and Oracle BI metadata and reporting. What I really like about the strategy is the ability to choose among 5+ retail BI modules and 25+ ERP/CRM BI modules to meet the unique needs of your particular retail enterprise, yet deploy that selection on a consistent and cohesive framework, and do so incrementally if desired.

Want to combine, say, Merchandising with Customer, Loyalty, Finance and HR to turn data from Retail, Siebel, EBS and Peoplesoft into information to drive business decisions? Want to, say, compare labor costs (HR) with sales per employee (merchandising)? Perhaps you have these Oracle apps and want to include supply chain BI coverage but don't own Oracle's supply chain apps? Oracle Retail Analytics is designed to also accept data from non-Oracle sources yet preserve the majority of packaged ETL transformations (ETL tends to consume 60-80% of the effort that goes into developing a BI/DW solution, and we want to pass as much of that value along as we can regardless of data source).

What also really excites me are the possibilities when running Oracle Retail Analytics on Exadata. While we've baked-in plenty of features to enable optimization of both loads and queries on Exadata, we've been careful to ensure great performance and scalability regardless of chosen platform (Exadata is optional). Since we've had the good fortune of being able to design from the ground-up using the very best and latest Oracle tech, at times we've felt like kids in a candy store. Designing "from the ground-up" has also enabled some features that otherwise would be difficult to design in a performant manner, like "as-is/as-was" reporting for the product and organization dimensions - allowing users to account for changes to these dimensions when assessing historical performance. So, as items are reclassified, or stores open, close, or move to new regions, reporting is done based on the dimensions as they were, and/or as they are.

Using Oracle BI 11g, Oracle Retail Analytics enables more than just viewing reports. It enables deep analysis including data mining (detailed, transaction-level data is retained) and in-context and embedded actions - so we have the ability to initiate an action right from a dashboard or report. These actions can include things like triggering a workflow to order more stock, or kicking off a promotion based on events or metric thresholds being crossed. Or, they can be simple things like notifying people of key information, guiding someone to do further analysis. We call this 'Closed Loop Analytics' - because it enables closing the loop between insight and action, and Oracle Retail Analytics is designed with this capability in mind.

If you're at Crosstalk in June, attend my session to learn more. --Mark

About


David Dorf, Sr Director Technology Strategy for Oracle Retail, shares news and ideas about the retail industry with a focus on innovation and emerging technologies.


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