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1、UNLOCKING THE POWER OF YOUR DATAA Deep Dive Into 5 Proven Data Monetization Methods for Retail and Consumer Goods Companies White Paper5 Proven Data Monetization Methods for SaaS Leaders2Data is a frequent buzzword for companies,from how it is collected and analyzed to the way it can be transformed

2、into business insights.While understanding these processes is crucial,another critical topic remains overlooked:data monetization.Retail and consumer goods companies could see significant benefits from monetizing their data.Given the amount of data(e.g.,customer data)typically collected by such comp

3、anies,it can enable new revenue streams,strengthen partnerships,and boost competitive positions.In this paper,we detail the reasons why retailers and consumer goods companies could do more with their data.We analyze five approaches to data monetization that such companies can employ and explore real

4、-world cases.Lastly,we discuss the key considerations to focus on when crafting an effective data strategy.DATA MONETIZATION IN RETAIL AND CONSUMER GOODS:WHY IT IS IMPORTANTIn todays highly competitive retail landscape,data monetization has emerged as a crucial strategy for retailers aiming to stay

5、ahead of the curve.By leveraging vast amounts of consumer data,retailers can unlock powerful insights that drive personalized marketing,optimize inventory management,and enhance customer experiences.As the retail sector increasingly shifts towards digital platforms,data becomes a valuable asset that

6、 can significantly boost revenue streams.According to recent studies,data-driven strategies are expected to contribute up to 25%of the total revenue for leading retailers by 2027,reflecting the growing importance of data in shaping retail success.By adopting data monetization,retailers not only gain

7、 a competitive edge but also create more meaningful and engaging interactions with their customers,leading to increased loyalty and higher sales.Retail executives will prioritize investments in data analytics in 2024Retail data monetization market volume in 2023Retail executives report having alread

8、y implanted extensive analyticsProjected retail data monetization market CAGR 2023-203244%1.1B USD40%17.1%RESEARCH STATSWhite Paper5 Proven Data Monetization Methods for SaaS Leaders3Established retail and consumer goods leaders have a significant,yet not entirely obvious advantage in areas entirely

9、 outside selling goods:your data.Realizing the additional value from your data assets can be a defining strategic differentiator.Selling online or through points-of-sale naturally accumulates three types of data:customer data,product data,and ancillary data.Customer data refers to the information ga

10、thered about buyers by retail and consumer goods companies through various channels.This data can be collected through direct sales(D2C/B2C,B2B),subscription models,and both online and brick-and-mortar interactions.This data includes,but is not limited to demographic profiles,personal preferences,sp

11、ending behaviors,and shopping histories.For example,by examining the data on consumer spending,one can identify patterns and trends in the sales pipeline.This insight allows businesses to forecast future sales more accurately,enabling better planning and strategic decision-making.Additionally,custom

12、er data can be used outside the specific retail domain.As an example,knowledge about demographics and what they are attracted to in certain locations can help adjust the types of ads and services offered at these locations.It can then also be applied to help other companies,for example,hotels,entert

13、ainment,healthcare,automobile,and other service-offering companies to target offers of their services and increase market share.Staying Ahead:The Untapped Potential of DataWhite Paper5 Proven Data Monetization Methods for SaaS Leaders4Ancillary data is the information collected and used for analytic

14、al purposes within organizations,apart from the data on customers and products or goods.It may include a history of weather conditions used to build relevant forecasting models,or supply chain disruption statistics used to assess delivery risks associated with certain suppliers and delivery routes.I

15、t can also assess average times required to serve a customer to optimize service levels,or to analyze customer movement in a store for planogram and shelf/rack re-stocking and category management.These types of data can be effectively commercialized,unlocking new revenue streams and business opportu

16、nities.It shows why a comprehensive data strategy should become an integral part of your overall company strategy.So how can retail and consumer goods companies maximize these opportunities and realize the full potential of these data resources?Read on to discover the five main methods of data monet

17、ization with successful examples from actual SoftServe clients.Product data,on the other hand,is information on goods sold through multiple channels.It shows demand and supply patterns and trends for specific goods,as well as customer feedback and reviews.This type of insight allows businesses to ad

18、just their assortments,pricing,and promotion policies based on the immense amount of information collected by other sellers of relevant goods.An example of product data includes tracking demand patterns for certain items to extend the use of this information to enable the adjustment of the supply of

19、 goods provided by manufacturers or distributors to the market.DIRECT MONETIZATION INDIRECT MONETIZATION Data-as-a-product Insight-as-a-productAnalytics-as-a-product Operational excellence External products and services DATA MONETIZATION STRATEGY White Paper5 Proven Data Monetization Methods for Saa

20、S Leaders5THE 5 MAIN APPROACHES TO DATA MONETIZATION DATA-AS-A-PRODUCT1WHAT IT ISThis approach is likely the first that comes to mind when hearing the phrase“data monetization.”Essentially,it is where retail and consumer goods businesses sell access to their data,either directly or via a data market

21、place like AWS Data Exchange or Databricks Marketplace.BENEFITS OF THIS APPROACH Retail and consumer goods companies that are market leaders will see the greatest benefits of using this data monetization method.The value here is proportionate to the market share your data represents,so smaller playe

22、rs may find it more difficult to see value.However,even without an extensive data reach,smaller organizations can reap the benefits by dipping into datasets of more mature companies.Analyzing data for various customer segments or use cases can allow any company to enhance its market reach and versat

23、ility.DataStrategyWhite Paper5 Proven Data Monetization Methods for SaaS Leaders6WAYS TO OVERCOME THE DRAWBACKSThe main downsides of the data-as-a-product approach to data monetization are twofold:The first problem can be managed by focusing on what sets your organization apart.You will need to dist

24、inguish your data products from other offerings for them to be of value.Because many data providers often offer standardized datasets,finding a relevant niche means delivering customized options.Personalized offerings that directly address specific challenges or requirements are critical,otherwise p

25、otential customers may find it difficult to justify their investments.1.The challenge of adding substantial value beyond the data itself.2.The risk of losing control over data assets after disclosing them.The second challenge of losing control of data assets can be largely mitigated using a data-sha

26、ring approach known as Data Clean Rooms.This offers a secure environment where both the data providers and the data users can collaborate and analyze data without directly exposing raw or sensitive information.Anonymizing and aggregating the data instead of sharing the raw version enables additional

27、 privacy.Enforcing strict access controls and usage policies so that only authorized users access the data further lowers the potential for misuse.It also differentiates you in a competitive landscape while extracting the maximum value from your data assets.The introduction of new revenue streams th

28、rough selling data as an individual product contributes to a more stable and profitable financial outlook.Data products also have the potential to generate long-term recurring revenue streams.Once developed,maintained,and marketed effectively,they can continue to provide value and generate ongoing r

29、evenue.White Paper5 Proven Data Monetization Methods for SaaS Leaders7EXAMPLE OF SOFTSERVE CLIENT USING CLEAN ROOMCompany:Supply chain risk management vendorData Assets:A global supplier database that includes real-time risk assessments derived from the ongoing analysis of corporate,political,weathe

30、r,and other relevant events.Monetization Approach:Granting existing customers privileged access to this database of suppliers and associated risk scores using a Data Clean Room solution.Value Add and Differentiator A detailed analysis of the suppliers role within the supply chain and of how signific

31、ant each supplier is to the clients operations.Following this assessment,this vendor then provides:An aggregate supply chain risk score that reflects the cumulative risks.Risk mitigation strategies that are tailored specifically to address the clients unique supply chain challenges.This monetization

32、 approach not only overcomes the potential risks of losing control over their data,but it also counteracts the other challenge of delivering added value.Integrating each suppliers significance into the overall risk calculation means that customers of this supply chain risk management vendor gain a d

33、etailed understanding of their potential vulnerabilities.This results in highly specific,actionable risk mitigation recommendations for the vendors clients.By offering its customers a tailored assessment that reflects their unique supply chain structure,this supply chain risk management vendor clear

34、ly distinguishes its data product from others.White Paper5 Proven Data Monetization Methods for SaaS Leaders8INSIGHT-AS-A-PRODUCTWHAT IT ISInsight-as-a-product takes the data-as-a-product method one step further.It allows businesses to internally analyze their vast data reserves and then provide the

35、 insights they uncover to other companies.This is more than just offering raw data it is about leveraging in-depth analysis to deliver valuable insights that drive strategic decisions.Some companies sell this intelligence as a premium service,often through a subscription model.While others share fin

36、dings as complementary analytics to boost brand awareness and lead generation.Two specific examples of this type of data monetization are:1.State-of-the-market reports,which aggregate data for a comprehensive overview.2.Benchmarking reports that compare a specific business directly to its average co

37、mpetitor.Due to the value added by processing raw data to make it strategically valuable,data providers can charge a premium compared to the standard data-as-a-product approach.BENEFITS OF THIS APPROACH One of the biggest benefits of this method is establishing brand authority and positioning yourse

38、lf as a thought leader in the industry,elevating firms a step above competing retail and consumer goods businesses.By providing high-quality insights,companies can boost their credibility beyond being businesses of choice for their customers to also spearheading data analytics in the industry.Like t

39、he data-as-a-product method,this approach allows a firm to maximize datas value,diversify revenue through new streams,and differentiate in a competitive market landscape.2White Paper5 Proven Data Monetization Methods for SaaS Leaders9DRAWBACKS OF THIS APPROACH AND WAYS TO OVERCOME THEMThe insight-as

40、-a-product approach faces fewer significant hurdles than data-as-a-product.As the insights are generated internally,raw data is not exposed,which avoids some of the biggest issues.One problem that can arise is the complexity of the insights.If the conclusions are overly technical and require special

41、ized expertise to interpret and apply effectively,this can limit their appeal.Customers without the necessary skills or knowledge may also struggle to derive actionable insights,reducing the perceived value and usability.A third issue can be the customization limits of the interpretations.To appeal

42、to a wide spread of customers,often from different industries,data providers might therefore have to offer more generic insights.However,if these perspectives do not address the needs or challenges of individual customers,they may again struggle to derive actionable revelations.Thankfully,these prob

43、lems can be overcome by helping your customers make informed decisions and drive positive outcomes.Focusing on recommendations,best practices,and practical guidance provides end users with a tangible value.By adopting these strategies,data providers can fine-tune and elevate their offerings.White Pa

44、per5 Proven Data Monetization Methods for SaaS Leaders10EXAMPLE OF SOFTSERVE CLIENT USING THIS APPROACH Company:Healthcare liability insurance company Data Assets:A comprehensive database of healthcare liability claims,with the associated raw healthcare provider data and additional codification.Mone

45、tization Approach:Delivering a quarterly insights report thats available only by subscription.Hosting an annual conference and highlighting yearly results and industry trends.Value Add and Differentiator 1.By converting comprehensive healthcare liability claims data into quarterly analytics reports

46、and offering an annual conference to discuss trends and forecasts,the company is providing far more than just raw data.2.The value lies not in the data itself but in the strategic intelligence derived from it.Using this approach helps set this healthcare liability insurance company apart and positio

47、ns it as an industry leader.In addition to distinguishing itself in a competitive marketplace,this company has also added additional revenue streams through subscriptions and event participation.White Paper5 Proven Data Monetization Methods for SaaS Leaders11ANALYTICS-AS-A-SERVICEWHAT IT ISWhile the

48、 insights-as-a-product approach focuses on answering general industry-wide questions based on internal data,the analytics-as-a-service method uses a customers data to provide tailored answers to specific questions.This approach may require more work than other methods as it needs a professional anal

49、ytics or services team that can manage both the analysis and interpretation required.Companies will need to build out their employee expertise if they plan to take on these requests to provide tailored analysis or custom data extracts.Overall,the analytics-as-a-service approach to data monetization

50、offers numerous benefits for retail and consumer goods companies,including new revenue streams,value-added services,differentiation,scalability,and improved customer insights.3White Paper5 Proven Data Monetization Methods for SaaS Leaders12CATEGORY/DEPARTMENTDATA LEVERAGEDPOTENTIAL CUSTOMER QUESTION

51、 TOPICS BENEFITS PROVIDED Ecommerce Retailer CPG CompaniesConsumer Engagement/Marketing DepartmentsWorkforce ManagementResource Management/ESGHealthcare Financial/Risk Management Transaction data Browsing behavior Demographic information Sensor data Production metrics Inventory levels Advertising da

52、ta Website traffic Customer interactions Employee data Performance metrics Demographic information Sensor data Equipment performanceCustomers patient data Market data Customer-collected transaction records Customer segmentation Product recommendations Sales forecasting Production optimization Supply

53、 chain management Quality control Campaign analysis Customer segmentation Attribution modeling Workforce planning Talent acquisition Employee engagement Energy efficiency Predictive maintenance Asset optimization Disease trends Treatment effectiveness Patient outcomes Risk identification Financial o

54、peration anomalies Personalized marketing efforts Improved customer engagement Increased sales Inefficiencies identified Processes streamlined Product quality enhanced Measuring campaign effectiveness Audience targeting Marketing spend optimization Data-driven decisions Improved organizational effec

55、tiveness Reduced costs Minimized downtime Optimized energy efficiency Improved care delivery Optimized resource allocation Reduced costs Fraud detection Risk management score calculation EXAMPLES OF HOW COMPANIES IN DIFFERENT INDUSTRIES COULD OFFER TAILORED ANSWERS TO CUSTOMER-SPECIFIC QUESTIONS USI

56、NG THE CUSTOMERS DATAWhite Paper5 Proven Data Monetization Methods for SaaS Leaders13BENEFITS OF THIS APPROACH One of the biggest benefits is that this approach allows for greater customization options.Customers can specify their preferences,priorities,and objectives,with the relevant and actionable

57、 insights derived from the data for their unique company.The flexibility of this also makes it attractive to customers with varying data needs and budgets.By leveraging these analytics services,customers can optimize their operations,identify opportunities,and mitigate risks more effectively.DRAWBAC

58、KS AND WAYS TO OVERCOME THEMWhen analysis is tailored for each customer,the customers data determines the parameters of your scope.While that helps avoid many potential legal issues,there can be concerns about data governance,privacy,and compliance when managing diverse datasets from multiple client

59、s.Firms will want to ensure that client and transactional data is managed securely,ethically,and is compliant with relevant regulations.Providing tailored analytics for each clients unique needs can also be complicated and resource-intensive,so firms will need the means and knowledge to execute this

60、 analysis before taking it to market.Scalability may present some issues since delivering customized analytics at scale is challenging.While expanding an analytics team to boost brainpower will help,businesses will also need to leverage cloud computing and scalable infrastructure solutions to enhanc

61、e workflows and data models.Investing in expanded personnel and infrastructure can make this cost-prohibitive for some providers.But if it creates the opportunity to stand out in the market,it can be worth the additional expense.White Paper5 Proven Data Monetization Methods for SaaS Leaders14EXAMPLE

62、 OF SOFTSERVE CLIENT USING THIS APPROACH Company:Provider of compliance monitoring for mortgage third-party originators.Data Assets:Database of lenders,compliance requirements,TPOs,and compliance statuses.Monetization Approach:Built a new revenue stream from a self-funded data analytics services tea

63、m.This professional services team leverages the companys extensive database and analytics capabilities to offer custom reports and data extracts that are not made available through“ordinary”company analytics.Essentially,clients who need specific data analyses or reports that go beyond the standard o

64、fferings can request these services.Offers an on-demand service model,with a separate statement of work for each customer request.This approach allows clients to request and receive custom data services as needed,without the commitment of a long-term contract while capitalizing on the unique data an

65、d analytics capabilities the data provider possesses.White Paper5 Proven Data Monetization Methods for SaaS Leaders15Value Add and Differentiator 1.The professional services team can offer a range of custom services,including advanced compliance risk analysis,predictive modeling to identify potentia

66、l non-compliance issues,benchmarking reports,and more in-depth analysis of the TPO landscape.2.With the on-demand service model,the data provider can also deliver precise insights and information not available through“ordinary”company analytics.This works best for clients with a hyper-specific need

67、such as an in-depth analysis of compliance trends among TPOs in a particular region.These monetization approaches offer high-value,tailored insights that help clients more effectively navigate the complexities of compliance management.By offering these advanced services,a data provider differentiate

68、s itself from competitors while positioning itself as an indispensable partner.It also deepens its relationships with existing clients by offering added value,while also attracting new customers seeking sophisticated compliance monitoring and analysis solutions.White Paper5 Proven Data Monetization

69、Methods for SaaS Leaders16WHAT IT ISWhile the previous three approaches focus on selling data in one form or another,this data monetization method is more indirect.Here,company data can be analyzed in depth to deliver actionable insights for internal benefit.For example,analyzing data on transaction

70、 frequency,customer interactions,and satisfaction metrics can highlight ways to improve customers NPS.It can identify common issues,trends,and opportunities for proactive actions and enable additional services to be provided.Such approaches enhance customer satisfaction and loyalty,reduce churn,and

71、increase customer lifetime value.Tracking,assessing,and measuring metrics for sales and marketing will also improve their ROI.This includes identifying the most effective selling strategies and promo policies,targeting the right audience segments,understanding buying behaviors,and refining messaging

72、 and sales efforts accordingly.With these measures,businesses can maximize the impact of marketing efforts,drive higher-quality leads,improve sales effectiveness,and increase conversion rates.OPERATIONAL EXCELLENCE4White Paper5 Proven Data Monetization Methods for SaaS Leaders17Optimizing operations

73、,improving product quality,and enhancing customer experiences can help differentiate a business and position it as an industry leader.Firms can respond faster to changing market conditions,customer needs,and competitive threats by monitoring KPIs,analyzing market trends,and adapting strategies accor

74、dingly.These benefits contribute to overall business success and long-term sustainability in a rapidly evolving market landscape.REDUCED COSTS Retail and consumer goods companies can identify and eliminate operational costs by streamlining processes,automating repetitive tasks,and optimizing resourc

75、e allocation.INCREASED PRODUCTIVITY Using data-driven insights to make informed decisions and prioritize initiatives,focuses efforts on high-value activities and drives higher productivity.MITIGATED RISKS Proactively address challenges and safeguard your business by identifying security vulnerabilit

76、ies,forecasting market trends,or anticipating customer churn.HIGHER CLIENT RETENTIONTailored products,services,and support interactions lead to higher customer retention and increased lifetime value.GREATER CUSTOMER SATISFACTION Leveraging customer data to deliver personalized experiences improves c

77、ustomer satisfaction and loyalty.EXPANDED GROWTH OPPORTUNITIES Enter new segments and expand sales using data-driven insights into customer preferences,and market opportunities.BENEFITS OF THIS APPROACH When using internal data,the analysis costs are minimal,but the business benefits are many.White

78、Paper5 Proven Data Monetization Methods for SaaS Leaders18DRAWBACKS AND WAYS TO OVERCOME THEMThe two most likely roadblocks to this indirect monetization approach are resource constraints and cultural resistance.Whether it is budget,talent,or infrastructure,limited resources or capacity can complica

79、te comprehensive internal data analysis.A lack of skilled data professionals or insufficient technology infrastructure can impede translating your data into insights for use in the decision-making process.Prioritizing investments in data analytics capabilities,talent acquisition,and technology infra

80、structure or developing partnerships with vendors to outsource certain functions will all help to alleviate this potential problem.Introducing data-driven practices and initiatives may also face resistance from employees who are accustomed to other approaches or skeptical of the benefits.Overcoming

81、cultural barriers and driving organizational change will therefore be easier if firms foster transparency,communication,and collaboration throughout the organization.Providing training and education empowers employees,while ongoing communication helps teams embrace the value of data insights.Establi

82、shing clear goals,metrics,and incentives will also better align expectations,unlock the full potential of data,and deliver business success.White Paper5 Proven Data Monetization Methods for SaaS Leaders19EXAMPLE OF SOFTSERVE CLIENT USING THIS APPROACHCompany:Retail analytics vendor,with a product po

83、rtfolio encompassing price optimization,promo campaign analysis,and assortment optimization.Data Assets:Raw data received from retail chain.Internal Inefficiencies:A disjointed product portfolio,with each product using its own data model,requiring separate integrations during the customer onboarding

84、 process.This not only complicates the integration process but also significantly extends the time required to onboard new customers.Monetization Approach:Use data from numerous customers to build a standardized retail enterprise data model that all portfolio products share.This unified data model w

85、ould serve as a single source of truth for all data interactions between the retail chains raw data and the analytics vendors array of products.By using this generic data model as the single data contract between the retail chain and the analytics product portfolio,the vendor reduces the number and

86、complexity of integrations.Instead of each product requiring its own integration process,all products can now interface with the retail network through a unified protocol.As a result,customer onboarding time dropped dramatically,from three to six months to just two weeks.White Paper5 Proven Data Mon

87、etization Methods for SaaS Leaders20Value Add and Differentiator Reduced Integration Complexity:With a singular data model,there is a standardized way to interpret and process data,eliminating the need for custom integrations for each product.Accelerated Customer Onboarding Time:The standardized dat

88、a model streamlines the integration process,leading to faster setup and deployment of the vendors services to the customers systems.Offering a more efficient and user-friendly integration process also helps differentiate this vendor from its competitors.Streamlining the integration process not only

89、speeds up onboarding but also leads to greater operational efficiencies as the vendor spends less time and resources managing multiple complex integrations.By adopting a unified domain enterprise data model,it resolved internal inefficiencies to leverage data assets more effectively,turning a challe

90、nge into a strategic advantage.White Paper5 Proven Data Monetization Methods for SaaS Leaders21WHAT IT ISThe least obvious and most frequently overlooked data monetization option for retail and consumer goods businesses is custom products and services.Here,a retail and consumer goods business utiliz

91、es the data collected from a point-of-sale and/or ecommerce platform,as well as B2B commerce,to create and launch entirely new products or services.Evaluating transactional data,customer feedback,and market trends offers a better understanding of customer needs and preferences and identifies pattern

92、s or bottlenecks.This approach is unique in that competitors cannot easily replicate the value proposition as they lack the necessary data.Whether new products or services are offered for free or have a price tag,they ultimately increase the brands value perception,reducing churn and increasing cust

93、omer satisfaction within the value chain.CUSTOM PRODUCTS AND SERVICES5White Paper5 Proven Data Monetization Methods for SaaS Leaders22CATEGORY/DEPARTMENTDATA LEVERAGED NEW CAPABILITIES OR PRODUCTS DEVELOPEDBENEFITS PROVIDED BY NEW TOOLS OR CAPABILITIES Supply Chain Management CRM Solution Asset Mana

94、gement or IoT Platform Financial Management or Accounting Energy Management or Sustainability Data collected from various supply chain processes Customer data collected from the CRM platformData collected from sensors and connected devicesFinancial data collected within the platform Data collected f

95、rom energy meters,sensors,and building management systemsSupply Chain Optimization PlatformsCustomer Insights and Analytics ToolsPredictive Maintenance SolutionsFinancial Planning and Analysis Tools Energy Management Solutions Using advanced analytics and machine learning algorithms,businesses can s

96、ee:Optimization of:Inventory levels Route planning Supplier selection Supply chain efficiency improvements Deeper insights into customer behavior Greater understanding of preferences Identifying customer trends Data-backed decision-making Improved targeting for marketing campaignsBy analyzing equipm

97、ent performance data,usage patterns,and environmental factors,businesses gain:Predictions of potential equipment failures Scheduling of proactive maintenance activities Reducing downtime and maintenance costs Firms gain insights into:Financial performance Budgeting Forecasting Scenario analysis Bett

98、er financial decision-making More accurate strategic planning Energy consumption monitoring Energy-saving opportunity identifications Energy usage optimization in facilities Energy cost reductions Carbon emission decreasesEXAMPLES OF HOW COMPANIES COULD USE DATA COLLECTED FROM CORE SOLUTIONS FOR NEW

99、 CAPABILITIES OR PRODUCTSBy analyzing and leveraging the data collected within their platforms,retail and consumer goods companies unlock new opportunities for innovation,differentiation,and revenue growth in various industries and domains.White Paper5 Proven Data Monetization Methods for SaaS Leade

100、rs23MULTIPLE BENEFITS OF THIS APPROACHVendors who launch new services or products that address specific customer pain points or needs will likely increase customer satisfaction and retention.Leveraging collected data to identify and respond to the needs of existing customers will further boost the c

101、ore value proposition.It can also deepen customer engagement and loyalty.Customers who rely on these additional offerings will be more likely to make repeated purchases,leading to higher customer lifetime value and revenue retention.Promoting these new offerings provides expanded upsell and cross-se

102、ll opportunities.This increases average revenue per user and drives revenue growth.Leveraging the data also highlights new market opportunities and ways of expanding into adjacent or complementary segments.From there,retailers can cater to unmet needs or emerging trends and tap into new customer seg

103、ments and markets.Using these benefits to foster a culture of continuous improvement and innovation within the organization will further solidify long-term sustainability and success.DRAWBACKS AND WAYS TO OVERCOME THEM As in the previous indirect monetization method,pursuing this approach can create

104、 a few problems.Developing new products or services using data insights requires significant resources.The need for highly skilled talent,innovative technology,and financial investments can mean constraints from budgets and personnel to material resources.However,to mitigate this,businesses can adop

105、t agile development methodologies and iterative approaches to better optimize resources or even re-allocate resources from other initiatives.Outsourcing some of these functions to a trusted partner such as SoftServe can also ensure investments in data analytics capabilities and technology infrastruc

106、ture go further.This is particularly the case if the data collected is fragmented across different systems or modules.With such data silos,integrating and consolidating disparate datasets to derive meaningful insights for new product initiatives can be challenging.Scaling data infrastructure to effi

107、ciently process and analyze large datasets while maintaining performance can be equally daunting.Expert partners like SoftServe can help overcome these integration and compatibility issues to drive revenue growth and brand differentiation.When firms proactively address these potential challenges and

108、 implement effective strategies,they will be able to successfully leverage proprietary data to deliver new product capabilities or introduce entirely new products.White Paper5 Proven Data Monetization Methods for SaaS Leaders24EXAMPLE OF SOFTSERVE CLIENT USING THIS APPROACHCompany:Vendor with a solu

109、tion for industrial machine fleet owners to order spare parts.Data Assets:A database of spare parts and their alternatives collected from the vendors order management solution,further enhanced with multiple companies product catalogs.Monetization Approach:With order management being a core offering,

110、the company introduced access to its extensive parts catalog as a separate service.This new service was separate from their core offering,providing customers with spare parts options,including alternatives and specifications directly sourced from company catalogs.Customers appreciated comprehensive

111、access to a vast database of spare parts and their alternatives to make more informed purchasing decisions.Eventually,this offering generated greater revenue than the initial solution.Value Add and Differentiator This service addressed a critical market need by facilitating more informed decision-ma

112、king for fleet owners in procuring spare parts.It allowed the vendor to meet evolving customer needs and enhance customer engagement and satisfaction.It also established a new and lucrative revenue stream and strengthened the vendors market position.The success of this monetization strategy highligh

113、ts the importance of understanding customer needs and the potential value of data assets when innovatively applied.White Paper5 Proven Data Monetization Methods for SaaS Leaders25Data monetization is not simply about revenue it is necessary as part of a comprehensive shift in how a retail business o

114、perates and innovates.To effectively leverage data,companies must cultivate a culture that encourages data-driven decision-making at all levels.This cultural shift starts with the leadership who set the tone for the organization by demonstrating the value of data-centric approaches.Training and upsk

115、illing employees in data analytics tools and methodologies will further embed data into the fabric of any company.Transparency and governance are equally pivotal.Ensuring that data usage is compliant with regulations and aligns with customer expectations is critical for building trust internally and

116、 externally.Fostering a Culture of Data Utilization Implementing a Data Strategy Workshop Understanding all that goes into building a data strategy and navigating the data monetization landscape can be daunting.To capitalize on these opportunities means taking deliberate steps towards understanding,

117、integrating,and operationalizing the data.That is where a data strategy workshop can empower a business with the guidance and tools needed to create and execute a comprehensive strategy.SoftServes Data Strategy Workshop combines our expertise with yours to create the most impactful and efficient ble

118、nd of data use cases and technical capabilities to realize them.Utilizing a collaborative approach,SoftServe has successfully helped companies across multiple segments to:IDENTIFY the most valuable data assets and available monetization opportunitiesDESIGN tailored use cases for data monetization th

119、at are strategic and actionable LEVERAGE technology integrations to enable the execution of the chosen strategiesESTABLISH clear metrics for evaluating the success of the data monetization efforts.With SoftServe,companies will gain in-depth analysis that can unlock new potential in the evolving data

120、 economy.Our data strategy workshops are collaborative,engaging,and designed to meld our technical expertise with your in-depth understanding of your market.Together,we will explore the vast possibilities your data offers,crafting a monetization strategy that is not just effective but uniquely yours

121、.White Paper5 Proven Data Monetization Methods for SaaS Leaders26CONCLUSION The road to data monetization is a dynamic and ongoing process.Businesses must continuously evaluate their data monetization strategies considering market trends,technological advancements,and shifting customer needs.The fut

122、ure belongs to those who can unlock their datas full potential.Those businesses that can weave data into their strategic planning and business operations will see a distinct competitive advantage.SoftServe understands that across the retail and consumer goods industry,standing out requires a keen un

123、derstanding of how to use your data.The five strategies we have discussed are the first steps in that process.From data-as-a-product to creating new customer products and services,these strategies drive innovation,add value,and create stronger connections with your customers.We look beyond the data

124、to reveal the story your data tells and the untapped potential it holds.Our goal is to help you transform it into actionable,revenue-generating strategies that set you apart.SoftServe is ready to prove that your data is more than numbers it is the future of your business,and with SoftServe,the futur

125、e looks bright.Now is the time to turn your data wealth into a thriving business asset.Contact us to speak to one of our retail data experts to learn how this can make your business more successful.NORTH AMERICAN HQ201 W.5th Street,Suite 1550 Austin,TX 78701+1 866 687 3588(USA)+1 647 948 7638(Canada

126、)EUROPEAN HQ30 Cannon StreetLondon EC4 6XHUnited Kingdom+44 333 006 4341 ABOUT USSoftServe is a premier IT consulting and digital services provider.We expand the horizon of new technologies to solve todays complex business challenges and achieve meaningful outcomes for our clients.Our boundless curi

127、osity drives us to explore and reimagine the art of the possible.Clients confidently rely on SoftServe to architect and execute mature and innovative capabilities,such as digital engineering,data and analytics,cloud,and AI/ML.Our global reputation is gained from more than 30 years of experience delivering superior digital solutions at exceptional speed by top-tier engineering talent to enterprise industries,including high tech,financial services,healthcare,life sciences,retail,energy,and manufacturing.Visit our website,blog,LinkedIn,Facebook,and X(Twitter)pages for more information.