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I recently attended the 2014 global analyst summit in San Francisco hosted by Pitney Bowes, an old technology company (now in business for 94 years) that has a new focus in its software along with an entirely new executive team. These leaders unveiled a business and technology strategy meant to demonstrate the company’s commitment to software. For many years it has been known mostly for mail services and postage metering, but Pitney Bowes also has made investments in software that can help companies change their business processes by optimizing their information assets. Over the past few years the company has had its ups and downs as regards its corporate mission, as I wrote in 2012. Most of the turmoil was due to conflicting agendas from past management, but other factors were the company was not as clear in communicating the value of its combined software portfolio and not capitalizing on the demand in lines of business and IT for information management and analytics software.

vr_Info_Optimization_10_reasons_to_change_information_availabilityPitney Bowes has several important assets. Its location intelligence software can provide consumers with accurate information about and directions to locations, enable businesses to target customers more accurately and help businesses be more responsive by adding a geographic context to customer information. Pitney Bowes also has advanced its efforts in information management to integrate and enrich data through spatial processing with a specialization in customer informationMy analysis of Spectrum, its information management software suite, found innovation in its support for analytical and operational use. Organizations are changing how they make information available as lines of business and IT share responsibility for improving information availability, which they do in 42 percent of organizations participating in our information optimization research; the most common reasons for doing so are to improve operational efficiency (cited by 67%) and to gain a competitive advantage (63%).

The latest version of the Spectrum Technology Platform includes data enrichment and quality in its data integration offering. It has advanced in search, query design, in-memory caching and support for Hadoop. The platform also is the foundation for a master information hub that can build relationship-based maps that Pitney Bowes calls knowledge graphs. These maps are more powerful than data relationship-based models that can’t map complex relationships and present them visually. In IT domains this is called master data management, but it goes beyond the usual entity relationship modeling to visualize and manage customer information from a business perspective; this can help bring business users into the process. It also can discover the locations of any information, which our information optimization research finds is important to more than one-fifth (22%) of companies. Having consistent information, particularly about customers, is critical for interaction across a business in providing the best possible customer experience. The Spectrum Platform also can process data in real time, which is attractive to the company’s new customers including large retailers and social media companies like Facebook. We find that only one-fourth (26%) of organizations are happy with their current technology used to provide information, which indicates an opportunity for Pitney Bowes to take a more aggressive position in the market.

vr_Customer_Analytics_02_drivers_for_new_customer_analyticsIn addition Pitney Bowes has customer and marketing analytics software called Portrait Analytics, which offers an engaging way to visualize and interact with customer information. It also can predict potential results through the Portrait Miner application. Another product, Portrait Uplift, can help companies can use the analytics to apply adaptive learning from customer interactions to model how customers will interact and where changes might be needed to stimulate purchasing and reduce churn. These applications have significant potential for marketing and other customer-focused functions because they do not require a data scientist to use them. In our next-generation customer analytics research more than half (59%) of participants said it is very important to improve them while only 15 percent are satisfied with their current efforts. Predictive analytics is the type of advanced analytics most important to 69 percent of organizations, and only one-fifth (22%) are happy with their current software. Pitney Bowes is in position to look for opportunities here as long as it focuses its efforts to the top drivers that are improving the customer experience (63%) and improve the customer service strategy (57%).

The company has not lost its focus on location intelligence software. Its flagship product here remains MapInfo, but it has made investments to highlight its potential for enterprise location intelligence by supporting it in social media and Internet location-based services. A series of new releases of MapInfo after the current version 12 are coming in 2014. The company says that enhancements will include the user experience, a more contextual interface, 64-bit processing, a layout designer, contextual menus and display management. To take advantage of multiple-core processing, Pitney Bowes has segmented processes to accelerate performance. Our latest research in location analytics found that reliability, which includes performance and scalability, is the third-most important software evaluation criterion. The updates also will expand mapping from vector-based to grid-based analysis. In addition mobile technology is a priority for MapInfo, as it should be: This is the second-most important technology innovation according to our location analytics research. MapInfo Stratus is a cloud-based extension of MapInfo Pro. Some support for mobile devices is available today, and more improvements to the experience are coming. This and other advances address the innovations that are changing computing and users’ expectations and are critical to keep MapInfo relevant.

vr_LA_location_analytics_delivers_business_valuePitney Bowes built its position in location intelligence on geocoding, integrating data sets across on-premises and cloud systems for access from a range of applications. Pitney Bowes includes 122 countries in its geocoding, and its software can provide multiple levels of accuracy based on what is available in a cascading approach. It also provides reverse geocoding, which helps identify locations through longitude and latitude; this is used by major social media. Pitney Bowes has advanced the capabilities so users can type ahead to find locations in proximity; this is especially critical for mobile application support. The ubiquity of mobile devices and Internet use and the growth of complex event processing and visualization are bringing new opportunities for Pitney Bowes’ geocoding and location processing. Our location analytics research finds that many organizations view current methods as not reliable, taking too many resources and too slow. Using a dedicated approach can deliver business value like improve customer experience (20%) and gain competitive advantage (17%).

vr_LA_dedicated_technology_provides_satisfactionPitney Bowes has announced the release of Spectrum Spatial, a location intelligence platform built on MapInfo. Additionally Spectrum Spatial Analyst is an interactive tool to examine spatial and location attributes of data. The Spectrum Spatial technology and Spectrum Spatial for BI are the basis for Pitney Bowes establishing new partnerships with IBM and SAP. Having location analytics with business intelligence is still rare and has potential to enhance business analysis as found in almost a third (30%) of organizations while using a dedicated approach with GIS and location analytics provides satisfaction in almost half (49%) of organizations.

Our big data research finds that only 16 percent of organizations are using geospatial analysis in big data analytics; we think this is an overlooked opportunity given the value of location in enhancing information assets. Pitney Bowes has focused on providing location intelligence to suppliers of standard RDBMSs and data warehousing, which are only a subset of the big data environment. But the company realizes the importance of big data and has announced expanded support for it in Spectrum to ensure it can rapidly access these new sources. It includes new in-memory support (50%) for SAP HANA and Hadoop (42%), which are the two types of big data support revealed as most important in our research.

vr_Big_Data_Analytics_15_new_technologies_enhance_analyticsOverall Pitney Bowes software combines data management and analytics with its specialization in location to meet today’s need to optimize information in real time to support both consumers and business decision-makers. In the past year it has gained traction with social media companies and other types of businesses through direct approaches. Its main challenges are that its brand is not known for solving these types of problems and that it has not been able to assert its presence through marketing. Our research on buyers and users of software for information management and big data analytics has identified demand for these capabilities; Pitney Bowes should use its own software to better market and sell its products as well as the technology deserves. Its new executives have made the strategy more clear; now it is time for the organization to execute it through more and better marketing to ensure that potential customers consider its products that deliver business insights.

Regards,

Mark Smith

CEO & Chief Research Officer

Sales forecasting is an essential process for most businesses. It helps guide the efforts not only of the sales function but also of finance, operations, manufacturing and customer service. Our recently released sales forecasting benchmark research reveals significant insights and best practices that can help companies optimize the effectiveness of this process. I recently wrote that most sales organizations need to make significant changes to the way they do sales forecasting. VentanaResearchBenchmark_SalesForecastingIn that analyst perspective, I examined aspects of technology that can make sales forecasting a much more efficient process than it is in most organizations that use software not designed for sales forecasting.

This research finds much indecision about making changes to improve the technology for sales forecasting. Half of organizations do not plan to change their vendor for sales forecasting in the next 12 to 18 months, and only 10 percent plan to change, although 8 percent will upgrade to the current vendor’s latest version. For organizations that are planning to change vendors, the most common reason is to speed up the forecasting process (54%); fully half of this group are not satisfied with their current product’s functionality. A substantial number of organizations are dissatisfied with their tools because data gets outdated quickly: One in four ranked this first. This discontent likely reflects the more rapid flow and greater volume of data organizations accumulate today and issues derived from the use of tools like spreadsheets into which people copy and paste data with no direct link to its sources. Yet the pace of business has accelerated along with the mass of data, and sales groups feel pressure to have timely forecasts; this challenge is another reason for improving the tools in use.

Organizations use desktop spreadsheets for many activities, and while they are easily accessible, spreadsheets are not designed for vr_SF12_03_sales_forecasting_technology_is_changingsales forecasting. Nevertheless the research shows that they are the most commonly used tool for sales forecasting (by 29%); sales force automation (SFA) ranked second, an increase from our past research. The third-most common tool used is analytics and business intelligence (BI, 10%). A range of planning and forecasting applications are used by 16 percent of organizations. Most of this technology was not designed to meet the specific needs of sales forecasting.

It is not surprising that fewer than one-quarter (24%) of the sales organizations primarily using spreadsheets for sales forecasting are satisfied with their process. Our analysis shows a correlation between spreadsheet use and lack of confidence in the information for sales forecasting: Fewer than one in three organizations relying primarily on spreadsheets (32%) are confident or very confident in the quality of their forecast. Moreover, more than half (59%) said that reliance on using spreadsheets makes it difficult to manage the sales forecast efficiently. Yet despite the evidence that spreadsheets have negative effects on sales forecasting, only 38 percent of heavy spreadsheet users are planning to change their forecasting process in the next 12 to 18 months, fewer than users of SFA (48%) and business intelligence tools (53%). When such an important process as the sales forecast is left to tools not designed for the process, risk increases that sales people will not have visibility into their information, let alone timely knowledge of progress and the source of issues that should be addressed.

On the other hand, the research finds that two in five (40%) organizations use dedicated tools for sales forecasting. Most that do are rather new to the technology; three in 10 have been using it for more than a year, and 10 percent more began in the last year. Currently dedicated sales forecasting software is deployed mostly to users on the front lines of sales: 42 percent of front-line sales managers and 41 percent of the front-line sales team have it available. Those that use dedicated sales forecasting technology find value in it: More than one-fifth (22%) of them said it has improved significantly the outcomes of sales activities and processes, and half (51%) indicated it has improved outcomes slightly. Larger organizations use such applications much more often than smaller ones: More than half (54%) of very large companies that use one have been doing so for more than a year, as have 46 percent of large companies. Another 29 percent of the very large started using a dedicated application in the last year, so nearly four out of five of this size of organization have dedicated software. But more than one-fifth of participants said they have no plans to deploy dedicated software. Asked why, most (58%) said they do not know, which indicates a lack of awareness of the technology and its advantages. One-fourth more (24%) said deploying it will not have a positive impact on business, which indicates a lack of understanding of its benefits. Dedicated applications can contribute to more accurate sales forecasts, but we find that many organizations aren’t prepared to implement them or do not understand why they should.

Our analysis finds good reasons for using dedicated technology. vr_SF12_11_teamwork_critical_for_sales_forecastingSales forecasting requires a team effort that involves account managers who track the sales pipeline, operations people and analysts who manage the process and sales managers who approve it and contribute information and metrics. According to our research the most important capabilities for the sales team in the process are to collaborate within the team to improve the sales forecast (selected by 53%), to compare quotas to the sales forecast (47%) and to perform account-level forecasting (40%). These activities make it possible for sales management to compare changes to the forecast, collaborate to address changes and determine actions, and take advantage of operating conditions in sales. The sales operations group processes the sales forecast, from preparing data through presenting the forecast to sales management, and we find that doing so takes a significant amount of time for this essential sales support team – time that could be spent better in applying a range of analytics to the forecast. Sales managers and management have their own priorities, which makes it important to profile the users of sales forecasting and align software capabilities to their roles and responsibilities. Making notations on why the forecast has changed and providing coaching based on the forecast are examples of tasks required to support the sales forecast process that are particularly difficult to do using a desktop spreadsheet.

vr_SF12_06_organizations_planning_to_change_forecastingOverall our research finds a developing movement to adopt more useful technology for supporting the sales forecasting process. For organizations planning to change, most (40%) are going to use software as a service (SaaS), which is convenient as this cloud-based approach has the lightest burden to install, configure and manage the technology. Nearly as many (35%) plan to use the on-premises approach, which could distract sales from its primary mission and the processes designed to support it. Being able to operate on mobile technology likely will spur adoption as more than half (59%) of participants indicated that mobile technology will help improve the accuracy and timeliness of sales forecasting. We urge organizations to use dedicated software for sales forecasting as a way to improve the outcomes of their sales efforts. Those that do not invest in such software will continue to face impediments in the sales process and risk not achieving the best possible outcomes.

Regards,

Mark Smith

CEO & Chief Research Officer

Mark Smith – Twitter

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