1、 2021 Gartner,Inc.and/or its affiliates.All rights reserved.CM_GBS_1142239The Future of Sales AnalyticsGartner for SalesA roadmap for sales operationsThe Future of Sales Analytics2Sales analytics functions often struggle to deliver desired commercial impact due to perennial challenges with low data

2、quality and poor stakeholder engagement.To achieve their full potential,sales operations leaders must prioritize data governance,data literacy and advanced analytics technology.Steve Rietberg Senior Director,ResearchSteve Herz Senior Director,ResearchAdnan Zijadic Senior Principal,ResearchThe Future

3、 of Sales Analytics3The Future of Sales AnalyticsOverviewKey Findings The need for collaboration among commercial functions is growing as buyer preferences evolve.From an analytics perspective,seller-provided CRM data may no longer provide sufficient intelligence on buyer behavior and intent.Sales a

4、nalytics functions that dont fully understand the information needs of the larger organization are missing the opportunity to share insights among commercial functions to drive more cohesive decision making.Fifty-three percent of surveyed organizations attribute poor sales data quality to inaccurate

5、 and incomplete data.Unfortunately,just 51%have established a formalized data governance body.A lack of data governance makes it difficult for sales operations to improve data quality and build trust in their analytical insights.The technology delivering sales analytics most commonly consists of rep

6、orts and dashboards residing in native sales systems.Expectations for unlocking analytical insights through more advanced technology,particularly by improving data integration and deploying artificial intelligence(AI),are on the rise.Sales operations leaders are challenged to identify the technologi

7、es most appropriate for their organizations.53%of surveyed organizations attribute poor sales data quality to inaccurate and incomplete data51%have established a formalized data governance body3The Future of Sales Analytics4RecommendationsSales operations leaders responsible for improving sales anal

8、ytics must:Ensure executive support for transforming the sales analytics function by designing a clear and compelling vision for sales analytics that reflects the needs of all commercial functions.Establish a formal data governance program to monitor and promote data quality,oversee analytics projec

9、ts and enable high-quality collaboration among all sales analytics stakeholders.Initiate a cross-functional data literacy program to ensure that consumers of sales analytics derive meaningful value and consistent interpretation.Develop a multiyear roadmap for sales analytics technology by identifyin

10、g and prioritizing specific use cases where advanced technologies offer the highest potential commercial impact.AnalysisSales operations leaders responsible for sales analytics are faced with a widening gap between their stakeholders need for data-based insight and todays status quo of data and anal

11、ytics.A number of factors are contributing to this growing challenge:Suppliers are facing unprecedented disruption and are looking to analytics to help them make sense of changing buyer behavior.Buyers are increasingly opting to interact with suppliers through digital channels,making it harder for s

12、ales analytics functions that rely on seller-provided pipeline data to glean insights.Sales operations leaders cite the complexity of business and its underlying data as a top obstacle for sales analytics,which will be exacerbated as new systems and data sources are added to the technology stack.Sup

13、pliers intend to invest in AI to take advantage of more predictive and prescriptive analytics,but are unsure where(i.e.,with which use cases)to begin that journey.The Future of Sales Analytics52021 Strategic Roadmap for Sales AnalyticsSales operations leaders can use our roadmap to set their vision

14、for bridging the gaps between their current and future states.Future state Augmented analytics and the decline of the dashboard A host of new data inputs unlocked by X analytics(technology that can detect,evaluate,extract and organize data from written text,spoken words and video recordings)A seamle

15、ss buying experience enabled by continuous intelligence Democratization of data science and AI Personalization displacing one-size-fits-all analyticsCurrent state Sales analytics primarily targeted to the sales function A common challenge posed by data governance Prevalence of native sales system re

16、porting Data literacy lowest at the seller levelGap Limited participation in analytics selection and design Analytics adoption inhibited by low data quality and trust Incomplete integration of channel interaction data Gaps in data literacy limit ROI on sales analyticsMigration plan Align stakeholder

17、s on a vision and prioritization of use cases.Establish data governance to enable collaboration.Elevate levels of data literacy throughout the organization.Prioritize technologies for specific sales use cases based on potential business impact.The Future of Sales Analytics6Current State of Sales Ana

18、lyticsSales analytics primarily targeted to the sales function.Sales operations leaders are clearly focused on delivering analytics insight to the sales organization as opposed to the remainder of the commercial organization.Data governance poses a common challenge.The importance of data governance

19、for sustainable success is widely recognized,but our research indicates that barely half(51%)of surveyed organizations have established a formalized data governance body.Native sales system reporting prevails.Unsurprisingly,a large number of organizations rely on native CRM/sales force automation(SF

20、A)reporting as their primary solution for delivering analytics to sellers and managers.Data literacy lowest at the seller level.Todays sales analytics functions are primarily focused on delivering on requests for reports and dashboards,but they neglect the need to improve the data proficiency and se

21、lf-sufficiency of those making the requests.The Future of Sales Analytics7Future State of Sales AnalyticsAugmented analytics and the decline of the dashboard A group of intelligent application capabilities,collectively known as“augmented analytics,”has emerged at the meeting point of three important

22、 SFA software trends.These trends data management automation,predictive sales analytics and natural language AI are enabling SFA systems to perform many of the error-prone,inefficient tasks that have stood between raw data and universal access to sales insights in the past.How augmented analytics ch

23、anges the sales analytics workflowCurrent sales analytics workflowAugmented analytics workflowKPIs and questionsData scientists prepare dataSales leaders explore predefined dashboards and KPIsSales leaders predict and prescribe action to sellersData scientists explore dataData scientists build views

24、 and dashboardsSales leaders interpret results,share findings and storiesSales leaders conduct root cause analysisActionAI performs augmented data preparationSales leaders explore predefined dashboards and KPIsPredictions and prescribed actions are served to sellersAI performs augmented data explora

25、tionAI serves best views and dashboardsFindings and insights are narrated in natural languageRoot cause analysis is autogeneratedActionKPIs and questionsThe Future of Sales Analytics8Future State of Sales Analytics“X analytics”unlocks a host of new data inputsGartner coined the term“X analytics”for

26、an emerging class of intelligent technology that can capture much of the unstructured business process information that has resisted measurement in the past.(The X is shorthand for the many data input types this model can accommodate,such as “text analytics,”“video analytics”or“audio analytics.”)Exa

27、mples of X analytics in sales can already be seen in conversational engagement analytics solutions,such as those recording and parsing audio and video communication streams to provide deal insights,competitive intelligence and pricing recommendations.Furthermore,SFA vendors are also embedding phone

28、call conversation data to provide input on sales coaching.Continuous intelligence enables a seamless buyer experienceContinuous intelligence(CI)is the real-time integration of analytical decision support directly into audiences day-to-day business activities.Live tracking signals extracted from sale

29、s activity data(such as email,natural language processing of phone call content,calendar invitation metadata and even handwritten meeting notes)are combined with current and historical data to derive decision-support insights.These are pushed to users just when they need them.BuyersSupplier go-to-ma

30、rketContinuous intelligenceBuyer dataSales and marketing activityThe Future of Sales Analytics9Future State of Sales AnalyticsDemocratization of data science and AIIn the near term,the sales analytics team needs specialists in data science to envision,develop and harness the potential of augmented a

31、nalytics,CI and similar innovations linked to AI.Very quickly,however,many of the specialized skills these experts provide will be replaced by automated analytics ecosystems that vendors are currently developing.Combined with advances in human interface capability,the native intelligence within tech

32、nology itself will make sales analytics audiences more autonomous on a day-to-day basis.Frontline and commercial leadership will enjoy seamless access to the insights they need to succeed.Meanwhile,the pressure on data experts to help deliver ad hoc support for stakeholders everyday challenges will

33、ease (as will the risk of bottlenecks).As barriers to end-user access recede,a democratizing wave of audience empowerment will ripple outward from the sales analytics program.Consumers of sales analytics,inside the sales organization and beyond,will depend less on sales analytics experts to understa

34、nd,model and answer many ad hoc questions.Personalization displaces one-size-fits-all analyticsThe expansion of AI technology will bring the differences in the analytic needs of different audiences into sharper focus.For strategic,centrally focused users of sales analytics(CSOs,C-suite peers and EVP

35、s of major divisions,for example),AI will enhance decision making by flagging patterns and forecasting broad outcomes better than humans are capable of doing.Technology advances will make district and region leaders better strategists and coaches by synthesizing customer needs at the district,territ

36、ory,account and opportunity levels simultaneously.Manager-facing analytics is an especially attractive use case for AI because of the advantages that an expanded dataset provides over the current limited line of sight.For sellers,augmented analytics functionality will improve short-and long-term dec

37、ision making.Except for the most standardized,transactional settings(e.g.,a high-volume call center),the value that sellers receive will come in the form of data-led decision support at the portfolio,account and opportunity levels.The Future of Sales Analytics10Align stakeholders on a vision and pri

38、oritization of use cases for sales analytics.Establish data governance to enable high-quality collaboration.Elevate levels of data literacy throughout the organization.Prioritize analytics technologies for specific use cases based on potential business impact.Migration PlanTo overcome gaps between t

39、he current and future state of sales analytics and to realize the benefits of that future state,sales operations leaders must focus on achieving a set of strategic objectives:These objectives may take years to fully achieve,as the effort could involve and impact all members of the commercial organiz

40、ation.The Future of Sales Analytics112023 Sustain data quality standards Prioritize analytics technologies20242022 Prioritize analytics use cases Assess current technology capabilities and identify gaps Create dictionary of metrics Execute data literacy learning curriculum2021 Set vision for sales a

41、nalytics with enterprisewide reach and relevance Define strategies for data governance,data literacy and technology Identify data dialects Triage data standardization activitiesStrategic Roadmap Timeline for Sales AnalyticsTo be achievable,the overall roadmap must be scaled down into more tangible a

42、nd attainable milestones.The timeline shown here is illustrative.The reality will depend on the organizations capabilities and appetite for change,and for some,activities may take quarters,not years.Drivers Cross-functional alignment Executive supportDrivers Formal data and analytics governance Comm

43、on language of dataDrivers Enterprise data literacy Technology implementationSource:Gartner11The Future of Sales Analytics12 2021 Gartner,Inc.and/or its affiliates.All rights reserved.CM_GBS_1142239Learn more.Dig deep.Stay ahead.Follow us on LinkedIn Gartner for SalesLearn more about Gartner for Sales at Transform Sales Ops for the Future webinar The State of Sales Analytics infographicBecome a Client