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Our benchmark research into retail analytics says that only 34 percent of retail companies are satisfied with the process they currently use to create analytics. That’s a 10 percent lower satisfaction score than we found for all industries combined. The dissatisfaction is being driven by underperforming technology that cannot keep up with the dramatic changes that are occurring in the retail industry. Retail analytics lag those in the broader business world, with 71 percent still using spreadsheets as their primary analysis tool. This is significantly higher than other industries and shows the immaturity in the field of retail analytics.

While in the past retailers did not need to be on the cutting edge of analytics, dramatic changes occurring in retail are driving a new analytics imperative:

Manufacturers are forming direct relationships with consumers through communities and e-commerce. These relationships can extend into the store and influence buyers at the point of purchase.  This “pull-through” strategy increases the power and brand equity of the supplier while decreasing the position strength of the retailer. This dynamic is evidenced by JC Penney, which positions itself as a storefront for an entire portfolio of supplier brands. Whereas before the retailer owned the relationship with the consumer, the relationship is now shared between the retailer and its suppliers.

What this means for retail analytics: Our benchmark research shows retail has lagged behind other businesses with respect to analytics. Given the new co-opitition environment with suppliers, retailers must use analytics to compete. Their decreasing brand equity means that they need analytics not just for brand strategy and planning, but also in tactical areas such as merchandising and promotional management. At the same time, retailers are working with ever-increasing amounts of data that is often shared throughout the supply chain to build business cases and to enrich customer experience, and that data is ripe for analysis in service to business goals.

E-commerce is driving a convergence of offline and online retail consumer behavior, forcing change to a historically inert retail analytics culture. As we’ve all heard by now, online retailers such as Amazon threaten the business models of showroom retailers. Some old-line companies are dealing with the change by taking an “if you can’t beat ’em, join ’em” approach. Traditional brick-and-mortar company Walgreens, for instance, acquired Drugstore.com and put kiosks in its stores to let customers order out-of-stock items immediately at the same price. However, online retailers, instead of looking to move into a brick-and-mortar environment, are driving their business model back into the data center and forward onto mobile devices. Amazon, for instance, offers Amazon Web Services and Kindle tablet.

What this means for retail analytics: There has historically been a wall between the .com area of a company and the rest of the organization. Companies did mystery shopping to do price checks in physical trade areas and bots to do the same thing over the Internet. Now companies such as Sears are investing heavily to gain full digital transparency into the supply chain so that they can change pricing on the fly – that is, it may choose to undercut a competitor on a specific SKU, then when its system finds a lack of inventory among competitors for the item, it can automatically increase its price and its margin. Eventually the entire industry, including midtier retailers, will have to focus on how analytics can improve their business.

Retailers are moving the focus of their strategy away from customer acquisition and toward customer retention. We see this change of focus both on the brick-and-mortar side, where loyalty card programs are becoming ubiquitous, and online via key technology enablers such as Google, whose I/O 2012 conference focused on the shift from online customer acquisition to online customer retention.

What this means for retail analytics: As data proliferates, businesses gain the ability to look more closely at how individuals contribute to a company’s revenue and profit. Traditional RFM and attribution approaches are becoming more precise as we move away from aggregate models and begin to look at particular consumer behavior. Analytics can help pinpoint changes in behavior that matter, and more importantly, indicate what organizations can do to retain desired customers or expand share-of-wallet. In addition, software to improve the customer experience within the context of a site visit is becoming more important. This sort of analytics, which might be called a type of online ethnography, is a powerful tool for improving the customer experience and increasing the stickiness of a retailer’s site.

In sum, our research on retail analytics shows that outdated technological and analytical approaches still dominate the retail industry. At the same time, changes in the industry are forcing companies to rethink their strategies, and many companies are addressing these challenges by leveraging analytics to attract and retain the most valued customers. For large firms, the stakes are extremely high, and the decisions around how to implement this strategy can determine not just profitability but potentially their future existence. Retail organizations need to consider investments into new approaches for getting access to analytics. For example, analytics provided via cloud computing and software as a service are becoming more pervasive help ensure they meet the capabilities and needs of business roles. Such approaches are a step function above the excel based environments that many retailers are living in today.

Regards,

Tony Cosentino

Vice President and Research Director

Karmasphere has an interesting story to tell. Much like Datameer, which I recently blogged about, Karmasphere sits on top of the Hadoop distributed platform where companies such as ClouderaHortonworks and MapR compete. Karmasphere provides a collaborative environment and an analytical workbench that help companies write applications and workflows that run on top of Hadoop. The company’s business model looks to leverage legacy skill sets, such as SQL, which are already resident in most organizations, in order to ingest, analyze and act on big data.

Karmasphere’s approach begins with the common assertion that business intelligence tools were built to analyze only structured data. They use descriptive statistics and provide historical views of data, but they are limited in their iterative discovery processes and in their ability to add new data in a timely, practical manner. Newer tools, such as in-memory databases and appliances, address the old technological limitations but have their own issues. While memory is becoming less expensive and speed is improved, proprietary hardware can lock businesses into a particular technology, and the way these tools manipulate data is often proprietary, too, in terms of how data is written back to disk and what types of data are stored on disk.

It’s through this gap in the market that Hadoop developers and companies like Karmasphere want to capitalize. Hadoop provides an ideal platform for companies exploring and analyzing big data because it is built to maximize disk I/O, run on commodity hardware and scale in a linear fashion. Given that commodity hardware by definition is fast, cheap and available, Hadoop clusters fit the bill for advancing big-data analytics.

The market is responding to this logic. Not only are we seeing venture capital pouring into the space, but our own benchmark research on Hadoop shows that it is being used in 33 percent of companies’ big-data environments and evaluated in another 20 percent. Very likely these numbers are increasing as I write this.

A big issue for Hadoop adoption is the skills gap. In our research on business analytics, 89 percent of participants said it is important to make it simpler to provide analytics and metrics to all users who need them. Last year big data was the domain of ninja data scientists creating MapReduce functions. Now, while big data is still a nebulous concept for many business people outside the data world, it is starting to come into focus, and the conversation is moving to the front lines of organizations. The discussion now is about how to match the power of Hadoop with the current skills of developers, data analysts, business analysts and end users.

Karmasphere addresses the skills issue. The company previewed its 2.0 release at the Hadoop Summit in June. The release focuses on the collaborative workspace and includes a shared repository for analytical assets in a role-based Web environment. With this team-oriented focus, Karmasphere is hitting a sweet spot in the market, as end users are becoming more involved in both usage and buying decisions. We’re looking forward to discussing this trend in our next-generation business intelligence benchmark research, due out soon.

As you might expect with an integrated Hadoop approach, Karmasphere 2.0 can ingest data from multiple sources, including data from Omniture and DoubleClick. Its wizard-based approach is helpful in identifying and consuming diverse data sources into the Hive tables native to Hadoop. The software enables users to do basic descriptive data exploration to determine the techniques and algorithms that they might want to apply. From there, analysts can conduct ad-hoc queries and iterative SQL analyses to test hypotheses and find insights from the data. Karmasphere also includes support for Hive UDFs and analytics packages such as SPSS and SAS for more complex analysis. Finally, Karmasphere helps users embed their insights in other systems through REST APIs and to publish through other BI applications such as Tableau and Spotfire.

By positioning the software more toward collaboration and analytics and less toward visualization, Karmasphere appears to be trying to carve out a unique space and avoid direct comparisons with companies such as Datameer that have advanced analytics and visualization capabilities. Karmasphere’s approach to visualization seems to be to provide some basics and partner with firms such as Tableau to provide more robust front-end user tools. This strategy allows Karmasphere to focus both its messaging and development efforts on the core value of bridging the Hadoop skills gap with analytics and collaboration tools.

Karmasphere has a strong story, but broader questions remain about how the Hadoop ecosystem will evolve from early adopters to early majority. A lot of money is still flowing into the Hadoop community, but I anticipate a shakeout in the space at some point, and only vendors that provide strong time-to-value propositions will remain intact. Karmasphere provides a compelling proposition around team analytic processes, collaboration and bringing the value of Hadoop to the front lines of organizations. By competing to fill the space between the technical aspects of Hadoop and the existing skills in the market, it manages to address a critical challenge around adoption.

Regards,

Tony Cosentino

VP & Research Director

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