Lightning is critical for Salesforce's service customers. When it was first announced, it stood out in the service and support industry as a bold attempt to assume control of the most important real estate in the business: the service agent's desktop. As interactions shift from a voice-centric environment to a multi-channel one, and as the sources of information used to coordinate a service response multiply, businesses are looking for platforms that can function as both full-fledged systems of record and systems of engagement. Salesforce has always had the former, and Lightning puts it squarely in the game for the latter. It's a cleaner UI, a strong development platform for partners, and it gives agents a single command post from which to run complex service interactions effectively. The question will be how long it will take for Salesforce Classic customers to transition over to the new Lightning experience. Adding the intelligence of Einstein and improved development platform will likely be a motivating factor for many businesses.
Salesforce's recently announced fiscal Q4 2017 revenue was up 27% to $2.3bn; Service Cloud brought in $651m of that, up from $540m in 2016. The company raised its fiscal 2018 guidance to revenue of $10.2bn, anticipating the fulfillment of a promise it's been making for some time to crack the $10bn revenue mark in the current fiscal year. The company has acquired about 15 vendors with specific machine-learning expertise since 2013. Salesforce estimates it has spent $600m in artificial intelligence (AI)-related acquisitions and other investments.
Products
Salesforce has been making important changes to Service Cloud since 2016. Now we are starting to see the results, notably in the form of a Lightning service console that delivers a new agent experience, improved productivity with Case Kanban view, Community Agent 360, federated search and macro builder to solve cases faster. The administration and setup provide a more agile environment, including drag-and-drop customization and the ability to extend with Lightning apps and components more easily.
The newer mobile app for customer service representatives provides native capabilities for iOS and Android. According to 451 Research's Voice of the Connected User Landscape (VoCUL) 2H 2016, 75% of survey respondents rated mobile applications for customer service and support as very important. Salesforce first put Lightning Snap-ins into the product to make it easier for developers to create integrated applications. It built SOS, a two-way mobile video support channel, which allowed customers to show service reps their issues, instead of just describing them.
In some ways, these changes reflected the new data-centricity of the service environment – contact centers are starting to pull in information from many more sources and channels and leverage them to supply better solutions to customers. Service is being structured less around the communication with the customer and more around the quality of the support being delivered. Salesforce was among the first vendors to recognize that the service environment is a gateway to other kinds of interactions, many of them potentially profitable, and its goal was to foster a way to leverage service to build more in-depth and lasting customer relationships.
Salesforce augmented Service Cloud further with the acquisition of HeyWire, which added mobile text messaging communications to the service process. Rebranded as LiveMessage following the acquisition, it allows customers to text to an 800 number or engage with service requests through social media messaging applications.
Earlier in 2017, Salesforce brought the Einstein AI engine directly into Service Cloud with the release of a set of applications that tie service infrastructure into enterprise data resources. For example, Einstein Case Management uses machine learning to automatically escalate and classify cases as they come in. High-priority cases can be quickly routed to the next available agent, and agents know what the case is about before they even pick up the phone, making the experience seamless for the customer.
On the management side, Einstein Supervisor gives managers real-time data on agent availability, queues and wait times, predicts customer satisfaction and makes specific recommendations to improve the customer experience.
Salesforce also introduced the native Field Service Lightning mobile app, a tool for connecting mobile employees such as field technicians with data resources back at the office. Using this application, companies can provide mobile employees with a service app on iOS and Android. The mobile app uses advanced algorithms to optimize scheduling and routing, provides real-time access to complete CRM data and has offline capabilities so mobile workers can be productive without cell coverage.
Tackling field service is an important step in Service Cloud's evolution. No service costs more to deploy than field service. Truck rolls consume enormous resources: skilled technicians are on the road for extended periods, and only a small portion of truck roles are actually customer-facing and productive. Field techs are also disconnected from home base, often without consistent access to product and customer data, or collaborative assistance. It's hard to have a 360-degree view of the customer if you're a tech in a customer's kitchen and you don't have the right part to repair a customer's appliance on your truck. Field Service Lightning brought all of the engagement elements together into a single platform – tools such as appointment booking and scheduling – and it allied them with the tools that were part of the Service Cloud that could track and optimize the field interaction, such as dispatcher tools, work order management and real-time tracking of field personnel.
In the latest release, announced in July, Salesforce has deepened the connection between Einstein and service resources with tools such as Einstein Vision for Field Service. Field Service Lightning will be able to use the AI engine's image recognition capabilities to provide field techs with real-time identification of parts and serial numbers from mobile photos. Einstein Vision for Field Service can quickly identify the exact product type from the photo and, in turn, save the repairman, customer and company time and resources.
Another component, Equipment and Inventory Management, leverages scheduling automation to ensure the correct work crew, equipment and trucks are where they should be. It uses awareness of the skills and supplies of each crew, along with their location, to help make decisions about which cases they should optimally handle.
Salesforce has been rolling Einstein out gradually over the last year, with attention mainly on how it can help in sales and marketing. The recent rollout of ABM features for lead scoring is a good example of where machine learning can move the needle on productivity. With field service, Salesforce has found an excellent use case for Einstein, one in which costs are extremely high, but the outcome of the interaction is especially important for the tenor of the customer relationship.
Competition
Salesforce's competition broadens each year as the company expands its portfolio. Additionally, all major business application providers are embracing aspects of machine learning and AI technologies. There are many vendors leading the charge. Besides Salesforce with Einstein, there is Oracle with Oracle Adaptive Intelligent Apps, IBM with Watson, Adobe's Sensei, Microsoft's Cortana, SugarCRM with Candace and OpenText with Magellan, just to name a few.
Salesforce's main competition comes from Oracle, SAP and Microsoft. In the service space, Salesforce competes directly with Oracle's Service Cloud (formerly NetSuite), as well as offerings from Zendesk, Freshdesk, ServiceNow, Verint and Agiloft.
Strengths
Salesforce's Service Cloud is a key part of its extremely broad customer experience portfolio. It incorporates applications for sales, marketing and service, and it is increasingly integrating between those three domains.
Weaknesses
There is still some lingering market confusion stemming from the flood of acquisitions, integrations and component branding efforts of the last few years. Additionally, customers that have custom developments on Classic using Visualforce have been slow to transition to Service Cloud Lightning.
Opportunities
Salesforce is in an excellent position to use service interactions as a way to wean buyers off their reliance on traditional contact center software, opening them up to an enterprise suite focused on data management rather than communication channels.
Threats
Many companies have already invested in some form of contact center solution, and additionally, stiff competition creates long sales cycles as companies transition to more modern customer service applications. Customer service departments are cost-constrained and conservative, and they may be slow to adopt new technologies, even those that are good for them.
Matt Aslett is a Research Director for the Data Platforms and Analytics Channel at 451 Research. Matt has overall responsibility for the data platforms and analytics research coverage, which includes operational and analytic databases, Hadoop, grid/cache, stream processing, search-based data platforms, data integration, data quality, data management, analytics, machine learning and advanced analytics. Matt's own primary area of focus includes data management, reporting and analytics, and exploring how the various data platforms and analytics technology sectors are converging in the form of next-generation data platforms.
Research Director Sheryl Kingstone focuses on improving the customer experience across all interaction channels for customer acquisition and loyalty. She helps operator and enterprise clients make decisions regarding the use of technology, business processes and data to boost revenue and optimize business performance. She also assists vendors with custom research projects, messaging and positioning, as well as product road map evaluations. Kingstone researches and writes on the top trends in mobile marketing and commerce along with cross-channel customer experience technologies.
Keith Dawson is a principal analyst in 451 Research's Customer Experience & Commerce practice, primarily covering marketing technology. Keith has been covering the intersection of communications and enterprise software for 25 years, mainly looking at how to influence and optimize the customer experience.