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PivotLink is a cloud-based provider of business intelligence and analytics that serves primarily retail companies. Its flagship product is Customer PerformanceMETRIX, which I covered in detail last year. Recently, the company released an important update to the product, adding attribution modeling, a type of advanced analytic that allows marketers to optimize spending across channels. For retailers these types of capabilities are particularly important. The explosion of purchase channels introduced by the Internet and competition from online retailers are forcing a more analytic approach to marketing as organizations try to decide where the marketing funds can be spent to best results. Our benchmark research into predictive analytics shows that achieving competitive advantage is the number-one reason for implementing predictive analytics, chosen by two-thirds (68%) of all companies and by even more retail organizations.

vr_predanalytics_benifits_of_predictive_analyticsAttribution modeling applied to marketing enables users to assign relative monetary and/or unit values to different marketing channels. With so many channels for marketers to choose among to spend their limited resources, it is difficult for them to defend the marketing dollars they allot to channels if they cannot provide analysis of the return on the investment. While attribution modeling has been around for a long time, the explosion of channels to create what PivotLink calls omnichannel marketing, is a relatively recent phenomenon. In the past, marketing spend focused on just a few channels such as television, radio, newspapers and billboards. Marketers modeled spending through a type of attribution called market mix models (MMM). These models are built around aggregate data, which is adequate when you have few just a few channels to calibrate, but it breaks down in the face of a broader environment. Furthermore, the MMM approach does not allow for sequencing of events, which is important in understanding how to direct spending to impact different parts of the purchase funnel. Newer data sources combined with attribution approaches like the ones PivotLink employs increase visibility of consumer behavior on the individual level, which enables a more finely grained approach. While market mix models will persist when only aggregate data is available, the collection of data in multiple forms (as by using big data) will expand the use of individual level models.

PivotLink’s approach allows marketers and analysts to address an important part of attribution modeling: how credit is assigned across channels. Until now, the first click and the last click typically have been given greatest weight. The problem is that the first click can give undue weighting to the higher part of the funnel and the last click undue weighting to the lower end. For instance, customers may go to a display advertisement to become aware of an offer, but later do a search and buy shortly after. In this instance, the last-click model would likely give too much credit to the search and not give enough credit to the display advertisement. While PivotLink does enable assignment by first click and last click (and by equal weighting as well), the option of custom weighting is the most compelling. After choosing that option from the drop-down menu, the marketer sees a slider in which weights can be assigned manually. This is often the preferred method of attribution in today’s business environment because it provides more flexibility and often reflects better the reality of a particular category; however,  domain expertise is necessary to apportion the weights wisely. To answer this particular challenge, the PivotLink software offers guidance based on industry best practices on how to weight the credit assignment.

Being based in the cloud, PivotLink is able to achieve an aggressive release cycle. Rapid product development is important for the company as its competitive landscape becomes crowded as on-premises analytics providers port their applications into the cloud and larger vendors look at the midmarket space for incremental growth. PivotLink can counter this by continuing to focus on usability and analytics applications for vertical industries. Attribution modeling is an important feature, and I expect to see PivotLink roll out other compelling analytics as well. Retailers looking for fast time-to-value in analytics and an intuitive system that does not need a statistician nor IT involvement, should consider PivotLink.

Regards,

Tony Cosentino

VP and Research Director

I had the opportunity last week to visit PivotLink in the Bellevue, Washington, office that houses the company’s development team and marketing leadership to see its software. After taking the helm a little more than a year ago and putting a new team in place, CEO Bruce Armstrong has positioned the company above the fray of the crowded business intelligence software set. The company has smartly moved into the retail space with user-friendly tools that should appeal to mid-tier retailers and where its historical success had been in the market. Building on earlier analysis on PivotLink and its advancement into analytics and cloud computing, this recent review focused more on its efforts to help the retail industry.

As we’ve discussed recently, marketing in retail environments is becoming more sophisticated thanks to forces such as cloud applications and shoppers’ mobile devices. This in turn is driving demand for a new class of analytics and technology to help with marketing optimization, attribution modeling, churn and share-of-wallet analytics, and merchandising analytics.

PivotLink addresses these challenges through software as a service (SaaS). Our recent benchmark research on business data in the cloud indicates that companies are increasingly adopting SaaS-based products across all lines of business. SaaS gives the advantages of shifting capital expenditures to operational expenditures, reduces IT involvement, and reduces Time-to-Value for technology adoption.

PivotLink’s RetailMETRIX provides 30 prebuilt reports and 60 best-in-class metrics that allow marketers to start using the tool right away. Some of its primary uses are to identify underperforming products or brands within a portfolio and to understand causal elements. This allows marketers to better anticipate customer demand and gives visibility into current trends in the supply chain.

Customer PerformanceMETRIX allows marketers to perform a range of analytics through user-friendly drill-downs, thereby producing ad hoc customer segmentations and enabling attribution and RFM analysis. Customer PerformanceMETRIX also integrates with third-party marketing systems to let users optimize campaigns against a particular merchandising strategy, then feed back the results into the PivotLink system in a closed loop process.

DataCLOUD provides data enrichment services by attaching customer-level data such as household demographics and psychographics. It can take into account big data sources such as social media data, including product-level and store-level reviews, as well as traffic and weather patterns. Such information is becoming more important in retail analytics as it allows businesses to assess the causes behind store-level performance.

PivotLink also provides mobile analytics with native capabilities for Apple and Android tablets. In our benchmark research on information applications, 51 percent of participants said broader access to information on mobile technologies is important or very important. In addition, our soon to be released benchmark on next generation of business intelligence that shows that many organizations use a broad spectrum of mobile devices. Users want all the capabilities inherent in those devices, which are available via native applications. Mobile capabilities should also appeal to PivotLink’s core constituency of retail customers, who often spend many hours out of the office traveling to various locations and suppliers.

PivotLink is in a position to capitalize on the changing environment for retail analytics. With most companies still using personal productivity tools to do their analysis, a turnkey SaaS solution with a low investment barrier makes a lot of sense in terms of time-to-value (TTV). Right now market momentum is shifting to cloud-based applications but there are still very few pure-play cloud-based retail analytics vendors. PivotLink must move quickly into this space before other cloud technology players seize the opportunity or larger players start to move downstream to make a heavier push into the mid-tier retail analytics space. I encourage retailers that are still doing analytics in spreadsheets to take a closer look at PivotLink and its approach to business analytics.

Regards,

Tony Cosentino

VP and Research Director

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

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