Datamatics.pdf
- 1. Using customer analytics to boost corporate performance Marketing Practice Key insights from McKinsey’s DataMatics 2013 survey January 2014
- 2. Contents Cross-referencing tools Indicates that additional information is available in the documentation part Indicates that the chart can be enlarged The following icons help readers to navigate in this report Introduction Part I: Executive summary Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? „ „ Analytics is not an IT but a strategic business topic „ „ A truly integrative approach is key „ „ High performers hire C-level executives with the “data gene” in their DNA Outlook – Implications for CEOs and CIOs Part II: Documentation Methodology and sample structure Detailed results „ „ Objectives and value contribution „ „ Capabilities and challenges „ „ Trends and future investments „ „ Organization and governance Contacts 4 5 8 8 11 12 16 17 20 20 24 26 28 29 2
- 3. Overview of key insights It creates value! Extensive use of customer analytics has a large impact on corporate performance. Companies that use customer analytics extensively are more likely to report outperforming their competitors on key performance metrics 1. 2. 3. 4. 5. Successful companies outperform their competitors across the full customer lifecycle. Focusing on acquisition and profitability gives the greatest leverage, yet none of the customer-relevant key performance indicators must be missed. Goals need to be set for both strategic and tactical indicators Having a culture that values and acts on customer analytics is critical. Investments in IT and skilled employees are also important, but investments alone will not deliver value. Leadership that expects fact-based decisions and an organi zation that can quickly trans late those facts into action are more likely to win than those companies that focus mostly on IT Success requires senior- management involvement in customer analytics. High-performing companies are led by data-savvy C-level executives who understand the importance of customer analytics and take a hands-on approach to the topic Integration of customer analytics across functions and channels is the top trend to focus on. Enabling integrated multichannel marketing that leverages real- time data and frontline access is seen as more important than leveraging new sources or types of data Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs 3
- 4. Introduction Corporations across the world and across industry sectors are increasingly approaching their busi nesses from a customer-centric perspective, amassing vast quantities of customer intelligence in the process. But harnessing this data deluge is a huge challenge. Even after drawing on sophisti cated software systems to portion big data into viable segments, countless companies are finding their expectations dashed. Big data often fails to deliver the big insights that were hoped for because com panies are not tackling the topic opti mally. To do this, it would be of huge benefit to know whether one can actually identify a corre lation be tween the use of customer analytics and cor porate performance. And if so, how can this be evidenced and quantified? Does the impact of customer analytics differ by industry? What capa bilities and investments are needed? What is the impact at stake? What are the most important levers? To find answers to these questions, McKinsey recently conducted a global survey on big data, inter viewing over 400 top managers of large inter national companies from a wide variety of indus tries. The data obtained consisted of com panies’ self-assessment of their own position and capa bil ities. A subsample of these results was then substantiated with objective performance criteria. The validation phase evidenced a signi fi cant correlation with the companies’ return on assets. Key aspects of the survey The DataMatics 2013 benchmarking survey was conducted from May to June 2103 with 418 senior executives of major companies distributed equally across Europe, the Americas, and Asia. All the organizations had revenues of between around EUR 500 million and over EUR 50 billion; the majority had revenues of over EUR 5 billion. Most respondents were from analytics-intensive sectors within 10 major industries, including Retail, Banking, Insurance, Media, IT, and Energy. The topics covered were the objectives and value con tribution of customer analytics, its capabilities and challenges, trends and future investments, and its organization and governance. This report describes the results of McKinsey’s 2013 DataMatics survey, explaining the cham pions’ secrets of success. It is divided into two sections. The first is a report covering the results of the study and some of the learnings that can be derived from it. The second section is more statistics driven, showing the data on which the report is based. Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs Key insights from McKinsey’s DataMatics 2013 survey Text box 1 Please refer to the Documentation section in Part II for further details on the survey. 4
- 5. Extensive and best-practice users of customer analytics outperform their competitors Use of customer analytics appears to have an im mense impact on corporate performance (Exhibit 1). The likelihood of generating above-aver age profits and marketing earnings is around twice as high for companies that apply their customer analytics broadly and intensively as for those who are not strong in customer analytics. The effect from sales is even greater: 50 percent of customer analytics champions are likely to have sales well above their competitors, versus only 22 percent of laggards. These champions are also almost three times as likely to generate above-average turnover growth as competitors who evaluate their data only sporadically (i.e., 43 percent of champions manage to do so, compared to only 15 percent of laggards). Return on investment shows roughly the same picture: companies making intensive use of cus tom er analytics are 2.6 times more likely to have a significantly higher ROI than competitors – 45 percent versus 18 percent. It is not just along such vital KPIs that these com pa nies are very likely to outperform their compe titors: they reveal a markedly higher likelihood of above-average performance across the entire cus tomer lifecycle. In terms of strategic KPIs, some of the findings are quite extraordinary. Intensive users of customer analytics are 23 times more likely to clearly outperform their competitors in terms of new customer acquisition than nonintensive users, and 9 times more likely to do so in terms of customer loyalty. Our survey results also show that the likelihood of achieving above-average profitability is almost 19 times as high for customer analytics champions as for laggards. Even more impressive is their likelihood of migrating an above-average share of customers to profitable segments, at 21 times that of non-intensive users of customer analytics (Exhibit 2). Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs Extensive users of customer analytics are more likely to outperform the market Extensive use of customer analytics has a large impact on corporate performance Percentage of companies above competition SOURCE: McKinsey, DataMatics 2013 1 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor". "Above competition" defined as 6 to 7 on a 7-point scale: 1 = Well below competition, 7 = Well above competition. 2 Based on "Please indicate how much you agree or disagree with the following statement: 'We use customer analytics extensively in our firm/business unit'." Scale of 1 to 7: 1 = Strongly disagree, 7 = Strongly agree. Comparison of items assigned 1 or 2 vs. 6 or 7. 22 20 15 22 49 45 43 50 +132% +186% +131% +126% ROI Sales growth Sales Profit1 Extensive use of customer analytics No extensive use of customer analytics2 Exhibit 1 5
- 6. A third of all survey participants rated customer analytics as extremely important for business success, positioning it among the top five drivers of their marketing. They consider it as important as price and product management, and only a few percentage points below service and actions to enhance customer experience, far ahead of the management of advertising campaigns (which only 20 percent view as a key driver of success). These results also differ considerably by industry sector. Banks have the greatest analytical skills, media companies are particularly strong on imple men tation, while the retail trade – surprisingly – lags furthest behind. Although they have an un precedented wealth of transaction data available, most retailers began deploying customer analytics comparatively late: industries such as financial services and telecommunications are far ahead. Extreme cost consciousness has held the majority of retailers back from making major investments in the field. Many also appear to lack awareness of how great the impact of customer analytics can be. While the best performers across all industries rate the use of customer analytics and other customer-oriented initiatives as the no. 1 contributor to their success, retailers whose performance is average view general marketing, pricing, and campaign management as the key factors for success – incorrectly. McKinsey’s benchmarking has shown that intensive data evaluation has the greatest impact on performance in the retail industry. In the European retail trade, benchmarking shows that the economic impact of customer analyses is in fact more than twice that in the banking sector, and around three times higher than in telecommunications and insurance. Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs Successful companies outperform their competitors across the full customer lifecycle 1 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor". "Above competition" defined as 6 to 7 on a 7-point scale: 1 = Well below competition, 7 = Well above competition. 2 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor." Aggregate index derived from the dimensions Sales, Sales Growth, Profit, ROI. Comparison of bottom vs. top quartile. Tactical KPIs 3 9 14 11 81 80 71 69 x 5.8 x 9 x 6.5 x 23 Customer satisfaction Customer loyalty Customers retained Customers acquired High performer2 Low performer2 5 3 74 4 10 63 76 75 x 21 x 15 x 18.8 x 7.4 Migration to profitable segments Value delivered to customers Customer profitability Sales to existing customers Performance index1 Strategic KPIs SOURCE: McKinsey, DataMatics 2013 Exhibit 2 6
- 7. Methodology The methodology used was to ask the participants to give full details of their context (position, indus try, geography, revenues, and number of staff), as well as their customer base and com pe titors. They were then asked a series of questions, rating their responses from 1 (Strongly disagree) to 7 (Strongly agree). These fell into various catego ries, such as the value contribution of cus tomer analytics, their objectives in using cus tomer ana lytics, and their use of it along different dimen sions, whether IT, analytics skills, or execution. Their evaluation of upcoming trends was also re quested, as well as their opinions on forthcoming challenges and opportunities. Investments, value chain management, and staff/governance were further aspects that were analyzed in detail. An extract from the McKinsey survey (below) is a brief example of the way many of the questions were structured. Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs Please refer to the Documentation section in Part II for further details on the methodology. 8 CS010 Why does your firm/business unit deploy customer analytics? Strongly disagree 1 (1) 2 (2) 3 (3) Neither agree nor disagree 4 (4) 5 (5) 6 (6) Strongly agree 7 (7) To acquire new customers (1) To increase sales to existing customers (2) To increase customer profitability (3) To increase customer satisfaction (4) To improve the value delivered to customers (5) To increase the proportion of loyal customers (6) To increase the number of customers retained (7) To migrate customers to more profitable segments (8) To reduce customer acquisition and servicing costs (9) For cross-selling purposes (10) To better understand the needs and wants of the customer (11) Other (please specify) (998)____________ Other (please specify) (999)____________ SO HOW DO THE TOP PERFORMERS DO IT? The findings showed that winners take a truly integrative approach, seeing analytics not as an IT but a strategic business topic. Hiring C-level executives that take a hands-on approach to customer analytics is also vital – they need to have Text box 2 7
- 8. Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs The findings showed that winners take a truly integrative approach, seeing analytics as a strate gic rather than purely as an IT issue. Hiring C-level executives who take a hands-on approach to customer analytics is also vital – they need to have the skills themselves, and be able to appreciate their importance. These three factors play a large role in the astounding spread in results between high performers and laggards evidenced in the previous section. Companies need to understand that it is not the IT that counts so much as what you do with it. Many managers associate customer analytics with complex IT systems and expensive analysis tools. It is true that no company can leverage their customer data successfully without IT investment. But relying on technology alone is not the answer. How companies actually make use of customer information is what makes the difference, and the organizational changes they implement to realize these changes. A narrow focus on technology and tools rather than staff and processes is another common failing. Competence is what counts – the ability to swiftly translate data into concrete action (Exhibit 3). That is where the retail industry – as just one example – falls down. It does not invest sufficiently in in-house expertise, staff skills, and the development of proprietary analysis models. McKinsey conducted a diagnosis on the advanced analytics capabilities of one of the leading global online companies, and outperformed their existing analytics in various algorithms during the following pilot phase. The new algorithms were fully imple So how do the top performers do it? Analytics is not an IT but a strategic business topic Exhibit 3 59 72 73 77 77 81 82 95 95 100 Broad use of customer analytics 91 Actionability of insights 92 Use of appropriate techniques 93 Management expectations Fast translation to action Fact-based decisions Interlinked IT systems Software accessibility 360°perspective Automated data processing Speed of model development Automated analytics Quality management of analytics In-house expertise 84 Management attitude 91 Analytics valued by the front line 91 Having a culture that appreciates and acts on customer analytics is critical for value creation Execution and organization 1 Pearson correlation of respective capability with value contribution of customer analytics (based on agreement with the statement "The use of customer analytics contributes significantly to our firm's/business unit's performance", with the highest scaled to 100%). Analytics IT Capability Importance for value contribution through analytics1 Percent Objective Understand the most important capabilities that enable an organization to gain value from customer analytics Method Correlate level of capability to perceived value contribution of customer analytics and index highest correlation of 65 to 100% Average for category 94 86 72 SOURCE: McKinsey, DataMatics 2013 8
- 9. Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs mented across multiple levers and channels, which led to a positive earnings impact of around EUR 1 billion per annum. This has substantially Example: A leading retailer uses thousands of automatically built predictive models to forecast future demand on an SKU level, and link it with their customer datamart. Automating as much of the analytics process as possible means the data scientists can focus their time on defining the analytics process and mission-critical QC and validation tasks. contributed to making the company a trail-blazing pioneer in the industry, epitomizing the “data to strategy” shift. Key capabilities for maximum value creation The key capabilities for creating value from cus tomer analytics are integrated deployment of IT systems, targeted analytics skills, and smart execution/organization. Ideal IT setup A company’s IT – including all relevant legacy systems – is all inter linked. Silos are minimized by ensuring intensive cooperation between the IT and business departments. A datamart with a 360° perspective on customers contains all customer data from multiple interconnected sources, and is accessible for analytics specialists from different business units. Data processing is fully automated, as well as analytics processes for critical marketing operations, with self-learning algorithms for stan dard analytics. All the requisite software is ac ces sible to analysts/analytics decision makers. Business users have access to software interfaces that allow them to run analytics on the go, without programing know-how. Text box 3 9
- 10. Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs Optimal analytics skills The company has in-house expertise for conducting advanced customer analytics. Analytics leverages both structured and unstructured data by combining data and text mining for ad vanced predictive models. Analytics infrastructure enables rapid development, evaluation and scoring of models from a single integrated environment. Different types of statistical and predictive algorithms are combined for maximizing the impact of analytics (e.g., by moving from traditional CHAID decision tree models to Random Forest or ensemble models to maximize accuracy). The analytics staff are excellent at deploying the appropriate analytics techniques and generating actionable insights from data. Standards for end product quality and service are enforced; predictive models are versioned and scores are tracked. Example: In the telecoms industry, analytics talent is a scarce resource for many organizations: analytics leaders score highest on superior quality manage ment of analytics and streamlining their analytics processes for maximum efficiency. Knowing this constraint, a leading telco player refrained from using the newest and most inno vative algorithms. Instead, they concentrated on finding the analytics talent able to apply the right analytics method for a clearly defined business question. Standards for end product quality are also precisely delineated and rigorously monitored. Highly efficient execution and organization New analytics insights are quickly translated into value-creating initiatives. Analytics assets are integrated into business processes (such as real-time scoring where necessary). Virtually everyone in the firm/business unit uses customer ana lyt ics-based insights to support decisions. Predictive analyt ics are used throughout the organization. Top management ex pects in sights stemming from customer analytics to sup port key mar ket ing and sales decisions. Frontline units value recom mendations based on customer analytics. Top management looks favorably upon using customer analytics to reach informed decisions. Example: A major insurance company improved their profits by integrating their customer analytics with their fraud detection system. During the claims handling process agents leverage customer analytics to fast- track claims for customer segments with a low likeli hood of fraudulent activities, simultaneously boost ing satisfaction and reducing operational costs. 10
- 11. Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs The integration of customer analytics across functions and channels is instrumental. The survey showed that enabling integrated multichannel marketing using real-time data and frontline access is more important than leveraging new sources or types of data (Exhibit 4). Successful big data players build an insight value chain that incorporates all elements from design through to the customer, end to end. The six elements in this chain are data, analyses, software, capabilities, processes, and strategy. Any chain is only as strong as its weakest link, so it is vital that each element is professionalized, and that they are optimally interlinked. The data have to be appropriately adjusted, structured, and enriched. The analyses also need to be reliable, supported by interactive and scalable software. The findings should not be bundled at one central site, but be made available to everyone involved in making the related decisions, accessible at any time. It is also important to continuously analyze the data for new sources of value, and to feed these into the value chain on a regular basis. Some aspects of the integration that participants rated as particularly significant are enabling integrated multichannel marketing, expanding customer analytics across the value chain, embedding analytics on the front line and processing real-time data. One of the UK’s major retailers started a customer analytics journey in the mid-1990s, further developing their analytics capacity on a continuous basis. They learnt to leverage analytics across a variety of marketing and sales levers, broadening these applications A truly integrative approach is key Exhibit 4 Respondents perceiving the trend as "extremely important"1 Percent Integration of customer analytics across functions and channels is the top trend to focus on 14 17 19 21 21 24 27 28 29 Frontline embedding of analytics Enabling integrated multichannel marketing Sharing data in strategic alliances Including third-party/external data Analyzing unstructured data Automating customer analytics Generating automated commercial actions Processing real-time data Expanding customer analytics across the value chain 1 What trends do you consider most important for customer analytics within the next 5 years? SOURCE: McKinsey, DataMatics 2013 11
- 12. Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs across their network to encompass targeted communications, pricing, and merchandising. Eventually, they even incorporated their suppliers so that production was directly influenced by the big data they were gathering. As a result, they managed to save hundreds of millions of pounds a year in promotion expenses while constantly increasing their market share, and customer com plaints fell by 75 percent. They have also seen a direct rise in customer loyalty: at present, 40 percent of their customers buy over 70 percent of their groceries at the store. The company’s unprece dented ability to translate data to insight to action created a sustainable competitive ad van tage, leading to clear market leadership, and exemplifies the integrative approach that is the secret to success. The results clearly indicate the importance of senior-management involvement in customer analytics. High-performing companies make sure of this by hiring C-level executives who understand the significance of customer analytics, and take a hands-on role in its deployment. High performers have 76 percent of their C-level executives involved in customer analytics, whereas the figure is only 45 percent at low performers – a difference of almost 70 percent (Exhibit 5). Perhaps unsur pris ingly given these results, 76 percent of com panies that extensively involve their senior man age ment in customer analytics strongly agree that custom er analytics significantly contributes to per for mance, while this is true of only 29 percent of com panies that do not. The likelihood of achiev ing an above- average return on investment is almost double High performers hire C-level executives with the “data gene” in their DNA Exhibit 5 The most profitable companies have top-management involvement and broader support for leveraging customer analytics Percentage of each level involved in customer analytics Percent Not/somewhat important 15 25 20 43 25 30 Working teams 39 Team leaders 51 Dept. heads 70 2nd level 53 Other C level 54 CEO 49 Very/extremely important 173 35 +394% Very/ extremely important Not/ somewhat important How important is the use of customer analytics to your commercial success? Number of FTEs actively engaged in supporting customer analytics Companies that rate customer analytics as important are more likely to have senior-level involvement and more FTEs involved SOURCE: McKinsey, DataMatics 2013 12
- 13. Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs that of laggards at companies where senior management is intensively involved in analytics, while the probability of generating above-average profits is more than twice as high (Exhibit 6). Sales growth is another key area where champions with senior-management involvement in customer analytics have a clear advantage, with almost three times the likelihood of outperforming competitors who do not give importance to this factor. Exhibit 6 Companies with senior-management involvement in customer analytics initiatives are more likely to outperform the market Senior-management involvement in customer analytics translates into business performance Profit1 38 +113% 81 31 +161% 81 43 +86% 80 43 +91% 82 Sales Sales growth ROI X% Percent of companies above competition 1 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor". "Above competition" defined as 5 to 7 on a 7-point scale: 1 = Well below competition, 7 = Well above competition. 2 Based on "To what extent is senior management involved in customer analytics initiatives?". Scale of 1 to 7: 1 = Not at all, 7 = To a great extent. Comparison of items 1 or 2 vs. 6 or 7. No senior management involvement in customer analytics2 Senior management involvement in customer analytics SOURCE: McKinsey, DataMatics 2013 While it is crucial that management walks the talk, it is also essential to anchor a culture of customer data management throughout the entire company, ensuring that analytics tools and their results are always connected to the right data, the right people, and the right marketing strategy. Everyone in the organization should understand the topic, and all decisions should be supported by analytics. 13
- 14. DataMatics and big data in action: All systems go A consumer goods retailer had a CRM organiza tion, but was using this on a reactive, ad hoc basis, only occasionally mining big data. The analyses were primarily descriptive – no use was being made of advanced analysis techniques such as customer value models or data mining. McKinsey’s holistic diagnostic and subsequent recommendations involved taking action in all functional areas: Sales, Marketing, Pricing, Category Management, and Inventory. Initiatives included expanding the client’s existing CRM system into an internal center of competence for cus tomer-oriented analytics and introducing standard reports to the business functions. What had previously simply been one technical tool among others was now elevated to the status of a strategic, predictive compass. Other measures were target group programs, intro ducing guided selling devices for branch staff, shopping apps for customers, and a Web shop with personalized content, plugged in along the entire customer journey. The project introduced tailored Web-based marketing mix modeling Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs that statistically links marketing expenditure and other drivers to sales (Exhibits 7, 8). This is not just historical, but can also estimate the sales impact of any stimulus, whether promotional price, halo advertising, social media or competi tive advertising. Pricing initiatives covered differ entiation by price zone, and the optimization of price guidelines for each category. Data-driven Exhibit 7 Action Target picture of big data competences after implementation of actions RETAIL EXAMPLE 1 Base level 5 Best practice Improvement +1 +1 Addressed by prioritized actions Data Software People Processes Strategy Average Analytics Machine room Embed- ding 2.7 3 3 3 3 3 3 3 2 1 2 2 2 3 3 3 2 2 3 Genera- tion of insights Targeted marketing Space Availa- bility 4 4 4 4 4 4 4 4 4 4 4 Pro- motion 3 3 3 3 3 3 1.5 2 2 1.5 2.5 3 2 2 3 2 4 3 Range Marketing mix Pricing 3 3 3 3 3 3 4 3.3 3 3 2.4 +2 +2.5 2 +2.5 +3 +3 +1 0 0 0 0 +1 +1 0 0 0 0 0 +1 +2 +2 +2 +1 +2 +1 +1.5 +2 +1 +1.5 +1.5 +1 +1.5 +2 +1 +1.5 +2 +1 0 0 0 0 0 +1 0 0 0 0 0 0 0 0 0 0 0 +1.0 +1.7 +1.5 +0.2 0 Pur- chasing SOURCE: McKinsey, DataMatics 2013 Text box 4 14
- 15. Outlook – Implications for CEOs and CIOs evaluation and development of categories was combined with out-of-range enquiries to produce leading-edge category and inventory management. The integrative aspect was key: the client found the EBTDA improvements that this initiative was likely to yield extremely compelling. Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Multiple actions are being taken to anchor this transformation into the organization on the dimensions of mindsets and behavior, skills and organization, tools and process integration, and data, IT, and analysis methods. Strong top- management commitment is particularly crucial, as well as ensuring that the analytical findings are woven into decision making at every level. Exhibit 8 EUR millions, Germany Complete implementation of the big data road map has a significant EBITDA impact 2014/15 RETAIL EXAMPLE EBITDA before one-off costs Total Marketing mix Targeted marketing Purchasing Availability Space Range Promotion Pricing Generation of insights 2013/14 Relevance of big data Implemen- tation effort Low High Actions Strategic relevance SOURCE: McKinsey, DataMatics 2013 15
- 16. Extensive and best-practice users of customer analytics outperform their competitors So how do the top performers do it? Outlook – Implications for CEOs and CIOs Whether termed “strategic analytics,” “business intelligence” or “customer analytics,” smart inter pretation of consumer data has already become an absolute must, not simply a nice-to-have value driver. From leveraging the converged cloud with intuitive interfaces to prescriptive models that match segments to campaigns, offers, and content using every conceivable channel, analytics is key to maintaining a competitive edge. In view of these developments, one can assume that every company will be performing customer analytics as a matter of course in the near future. The call to action for CEOs/CIOs of laggards will therefore be to diagnose their status quo and catch up as swiftly as possible. But champions will not be able to rest on their laurels. Since all the players will be in on the game and improving by the minute, champions need to prepare themselves for a pure-play strategy that elevates them to operational excellence, with the lowest costs coupled to the greatest possible benefits. Operationalizing the insight value chain along every link will be the key. Catapulting them selves into the next dimension of integrative data analysis that encompasses the entire ecosystem of industry players will not be easy, but the rewards will far outweigh the effort. Outlook – implications for CEOs and CIOs 16
- 17. Methodology and sample structure Methodology and sample structure Detailed survey results Documentation The DataMatics 2013 benchmarking survey was conducted from May to June 2103 with 418 senior executives of major companies from a wide variety of industries and distributed equally across Europe, the Americas, and Asia. The data obtained consisted of companies’ self-assessment of their own position and capabilities. A subsample of these results was then substantiated via correlation with objective performance criteria. The validation phase evidenced a significant correlation with the companies’ return on assets. SOURCE: McKinsey, DataMatics 2013 Key parameters of the survey What senior executives need to know about customer analytics (examples) ▪ What is the value contribution of customer analytics? ▪ What capabilities are essential to take customer analytics to the next level? ▪ What are the top trends? ▪ What needs to be invested in customer analytics to be competitive in the future? ▪ 418 senior and C-level executives participated in the online survey ▪ Field time was from May to June 2013 ▪ The survey covers a variety of industries, including Retail, Banking, Insurance, Media, IT, and Energy ▪ The survey was conducted globally, equally distributed across Europe, the Americas, and Asia Contacts Exhibit I The DataMatics benchmarking survey was conducted to answer key questions of senior executives on customer analytics (Exhibit I). 17
- 18. Methodology and sample structure Detailed survey results Documentation The sample covered key customer analytics topics (Exhibit II) … … as well as a broad range of industries and geographies (Exhibit III). Exhibit II Exhibit III Region Industry Level of respondents 32 39 29 5 5 6 15 16 7 10 10 12 14 4 5 6 18 22 6 9 13 17 SOURCE: McKinsey, DataMatics 2013 IT/Software Insurance Pharma/Chem/Med Media, Entertainment & Information Advanced Industries2 Telecom Other1 Retail/Apparel Energy Banking and Securities CMSO CSO Head of Sales CMO CEO Other3 Head of CRM CCO Head of Marketing Americas Asia EMEA 1 E.g., Hospitality, Logistics, Agriculture, Education. 2 Automotive, Manufacturing, Construction. 3 E.g., Sales Director, Head of Marketing Analytics, Manager, VP. ▪ Equal distribution of responses across Europe, the Americas, and Asia ▪ Majority of respondents from analytics-intensive industries ▪ Strong focus on C-level executives Percent, n = 418 SOURCE: McKinsey, DataMatics 2013 Key questions asked Topics covered Capabilities and challenges in customer analytics ▪ Assessment of capabilities in IT, analytics, and execution ▪ Biggest challenges and opportunities ▪ Industry leaders in customer analytics Trends and future investments ▪ Most important trends ▪ Customer analytics as a share of the overall marketing budget ▪ Changes in spending Organization and governance of customer analytics ▪ FTEs in customer analytics ▪ Level of senior management involved ▪ Performance indicators for customer analytics Objectives and value contribution of customer analytics ▪ Overall importance ▪ Value contribution ▪ Objectives pursued Contacts 18
- 19. Methodology and sample structure Detailed survey results Documentation The respondents were mainly recruited from large organizations (Exhibit IV). Exhibit IV > 50,000 5,001 - 50,000 ≤ 500 501 - 5,000 28 21 27 24 3 2 8 0 - 10 21 - 30 41 - 50 31 - 40 11 - 20 11 11 29 > 50 > 50,000 5,001 - 50,000 501 - 1,000 ≤ 500 1,001 - 5,000 27 13 16 31 13 Employees Percent Share of overall marketing budget spent on customer analytics Percent Revenues EUR millions 6 6 66 22 ≥ 5,000 < 500 < 50 < 5,000 Ø = 147 FTEs FTE employees actively supporting customer analytics Percent SOURCE: McKinsey, DataMatics 2013 Ø = 20.5 Contacts 19
- 20. Detailed survey results Methodology and sample structure Detailed survey results: Objectives and value contribution Documentation For companies to tackle the topic of big data optimally, the key is to know that there is actually a correlation between the use of customer analytics and corporate performance. But the answers to other questions are vital, too. To what extent can this be evidenced and quantified? How does the impact of customer analytics differ by industry? What capabilities and investments are needed, what is the impact at stake, and what are the most important levers? McKinsey’s detailed survey results address these topics, broken down into four categories: objectives and value contribution, capabilities and challenges, trends and future investments, as well as organization and governance. Objectives and value contribution Extensive use of customer analytics plays a large role in driving corporate performance (Exhibit V). Extensive users of customer analytics are more likely to outperform the market Percentage of companies above competition1 SOURCE: McKinsey, DataMatics 2013 1 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor". "Above competition" defined as 6 to 7 on a 7-point scale: 1 = Well below competition, 7 = Well above competition. 2 Based on "Please indicate how much you agree or disagree with the following statement: 'We use customer analytics extensively in our firm/business unit'." Scale of 1 to 7: 1 = Strongly disagree, 7 = Strongly agree. Comparison of items assigned 1 or 2 vs. 6 or 7. 22 43 15 20 22 45 50 49 +132% +186% +131% +126% ROI Sales growth Sales Profit Extensive use of customer analytics No extensive use of customer analytics2 Contacts Exhibit V 20
- 21. Methodology and sample structure Detailed survey results: Objectives and value contribution Documentation Successful companies are more likely to outperform competitors across the full customer lifecycle (Exhibit VI). The more mature the customer analytics approach, the stronger its contribution is likely to be to corporate performance (Exhibit VII). Exhibit VI Exhibit VII SOURCE: McKinsey, DataMatics 2013 1 Based on "Please indicate how much you agree or disagree with the following statement about the contribution of customer analytics to your firm's/business unit's performance: 'The use of customer analytics contributes significantly to our firm's/business unit's performance'." Scale of 1 to 7: 1 = Strongly disagree, 7 = Strongly agree. 2 Based on "Please indicate which of the following best describes the level of analytic development of your firm/business unit." 43 24 13 11 5 18 +37 percentage points High … Low The use of customer analytics contributes significantly to our firm's/business unit's performance1 Percentage of respondents who strongly agree Analytics maturity2 The highest absolute increase is at the last step (+19%); excellence in customer analytics drives disproportionate value contribution High performance on all dimensions; goals need to be set for both strategic and tactical indicators 1 Based on "Please describe the performance of your firm's/business unit's marketing group in the following areas relative to your average competitor (consider the immediate past year in responding to these items)". Above competition defined as 6 to 7 on a 7-point scale: 1 = Well below competition, 7 = Well above competition. 2 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor". Aggregate index derived from the dimensions Sales, Sales Growth, Profit, ROI. Comparison of bottom vs. top quartile. Tactical KPIs 9 3 14 x 5.8 x 9 11 x 6.5 x 23 Customer satisfaction 81 Customer loyalty 80 Customers retained 71 Customers acquired 69 High performer Low performer2 3 5 4 x 21 x 18.8 10 x 15 x 7.4 Migration to profitable segments 63 Value delivered to customers 76 Customer profitability 75 Sales to existing customers 74 Strategic KPIs SOURCE: McKinsey, DataMatics 2013 Performance index1 Contacts 21
- 22. Methodology and sample structure Detailed survey results: Objectives and value contribution Documentation Also, companies that actively measure their campaign performance with quantitative metrics are more profitable (Exhibit VIII). The competitive and market context is a hugely important driver: the stronger the related external factors are perceived to be, the more intensively companies apply customer analytics (Exhibit IX). Exhibit VIII Exhibit IX Extensive use of customer analytics is primarily driven by the competitive and market context Companies stating Top2Box Percent1 SOURCE: McKinsey, DataMatics 2013 1 Based on "Please indicate how much you agree or disagree with the following statements: 'Our customer base is very diverse'; 'Our customers' needs and wants change frequently'; 'Customer analytics is used extensively in our industry'; 'Our firm faces intense competition'." 2 Based on "We use customer analytics extensively in our firm/business unit". Scale of 1 to 7: 1 = Strongly disagree, 7 = Strongly agree. Definition of extensive use of analytics: comparison of items assigned 1 or 2 vs. 6 or 7. 47 0 3 35 70 87 100 52 Competition Diverse customer base Used extensively in industry Change of needs and wants No extensive use of customer analytics Extensive2 use of customer analytics Performance of companies using different metrics Share of highly profitable companies, percent1 1 Based on "Please describe the performance of your firm's/business unit's marketing group in the following areas relative to your average competitor (consider the immediate past year in responding to these items)". Scale of 1 to 7: 1 = Well below our competition; 4 = About equal; 7 = Well above our competition. Values 6 and 7 are classified as "highly profitable". 2 Based on "Which campaign performance metrics does your firm/business unit use?". Companies applying quantitative marketing campaign metrics are more profitable SOURCE: McKinsey, DataMatics 2013 28 29 31 23 25 39 40 42 42 41 Profit uplift Reach Response rate Campaign ROI Revenue uplift Using metric2 Not using metric2 Contacts 22
- 23. Methodology and sample structure Detailed survey results: Objectives and value contribution Documentation Customer analytics is most often focused on customer retention and loyalty. This leaves a great deal of as yet untapped potential in fields relating to sales opportunities and customer understanding (Exhibit X). Exhibit X Why does your firm/business unit deploy customer analytics? Percentage who highly agree (6 or 7 on a scale of 1 - 7) 36 39 42 49 49 50 50 51 53 55 61 To reduce customer acquisition and servicing costs To migrate customers to more profitable segments For cross-selling purposes To improve the value delivered to customers To increase customer profitability To increase customer satisfaction To acquire new customers To better understand the needs and wants of customers To increase the number of customers retained To increase the proportion of loyal customers To increase sales to existing customers SOURCE: McKinsey, DataMatics 2013 Focus on retention and loyalty Sales opportunities (acquisition, profit- ability, cross-selling) and customer under- standing (needs, satisfaction, value delivered) Contacts 23
- 24. Methodology and sample structure Detailed survey results: Capabilities and challenges Documentation Exhibit XI Exhibit XII SOURCE: McKinsey, DataMatics 2013 1 Based on "What do you see as the greatest challenges that your firm/business unit will face within the next 5 years in the area of customer analytics?". Challenges seen in customer analytics over the next 5 years1 The biggest challenge is to build analytic capabilities and integrate them into the demand generation process. Capabilities Access to reliable data in real time and ensuring data protection and privacy concerns are key to success. Data It is vital to embed a culture of customer analytics across the organization and enable users to easily access customer analytics relevant to their functions. Company culture The issue we are facing is to embed customer analytics into everyday frontline operations. Implementation The whole market faces intense competition in attracting customers. It is getting harder to stay ahead. Cost/markets 59 72 73 77 77 81 82 95 95 100 Broad use of customer analytics 91 Actionability of insights 92 Use of appropriate techniques 93 Management expectations Fast translation to action Fact-based decisions Interlinked IT systems Software accessibility 360°perspective Automated data processing Speed of model development Automated analytics Quality management of analytics In-house expertise 84 Management attitude 91 Analytics valued by the front line 91 SOURCE: McKinsey, DataMatics 2013 Execution and organization 1 Pearson correlation of respective capability with value contribution of customer analytics (based on agreement with the statement "The use of customer analytics contributes significantly to our firm's/business unit's performance", with the highest scaled to 100%). Analytics IT Capability Importance for value contribution through analytics1 Percent Objective Understand the most important capabilities that enable an organization to gain value from customer analytics Method Correlate level of capability to perceived value contribution of customer analytics and index highest correlation of 65 to 100% 94 86 72 Average for category Capabilities and challenges Having a culture that appreciates and acts on customer analytics is critical for value creation (Exhibit XI). Key challenges can be grouped into five areas: costs and markets, data, capabilities, implementation, and company culture (Exhibit XII). Contacts 24
- 25. Methodology and sample structure Detailed survey results: Capabilities and challenges Documentation Exhibit XIII SOURCE: McKinsey, DataMatics 2013 1 Based on "What do you see as the greatest opportunities ahead for your firm/business unit within the next 5 years in the area of customer analytics?". We definitely have opportunities to increase our customer expe- rience with a focus on the long-term value of individual customers. Better customer insights could help us anticipate customer needs and improve the conversation with our customers. Granular customer insights allow the customization of products, prices, and user experience along all touch points. Opportunities perceived in customer analytics over the next 5 years1 Opportunities are mainly seen in improving customer experience and multichannel integration (Exhibit XIII). Contacts 25
- 26. Methodology and sample structure Detailed survey results: Trends and future investments Documentation Exhibit XIV Exhibit XV 11 11 13 15 16 IT (software, hardware, licenses for software tools) Overall change in customer analytics spending Use of external consultants Human resources (customer analytics staff) Data acquisition (fees for acquisition of third-party data and data collection) 10 13 13 16 18 SOURCE: McKinsey, DataMatics 2013 1 Based on "Compared with the past 12 months, how will your company's/business unit's customer analytics spending change (as a percentage) over the coming 12 months?". Expected increase in spending over the next 12 months Percent1 One-off expenses Ongoing expenses Respondents perceiving the trend as "extremely important"1 Percent 14 17 19 21 21 24 27 28 29 Frontline embedding of analytics Enabling integrated multichannel marketing Sharing data in strategic alliances Including third-party/external data Analyzing unstructured data Automating of customer analytics Generating automated commercial actions Processing real-time data Expanding customer analytics across the value chain SOURCE: McKinsey, DataMatics 2013 1 Based on "What trends do you consider most important for customer analytics within the next 5 years?". Integration of customer analytics across func- tions and channels is seen as extremely important Acquisition of additional data sources is viewed as less important Trends and future investments Integration of customer analytics across functions and channels is the top trend (Exhibit XIV). Spending on customer analytics is expected to increase across all dimensions in the near future, with a focus on IT and data acquisition (Exhibit XV). Contacts 26
- 27. Methodology and sample structure Detailed survey results: Trends and future investments Documentation Exhibit XVI 26 19 9 21 Customers acquired2 25 18 13 21 Sales to existing customers2 25 19 11 21 Customer profitability2 23 19 13 21 Customer satisfaction2 25 18 21 21 Value delivered to customers2 24 18 22 21 Proportion of loyal customers2 24 19 11 21 Customers retained2 28 18 9 21 Customer migration to more profitable segments2 Share of marketing budget spent on customer analytics Percent1 Top tier Middle tier Low tier 1 Based on "Considering the immediate past year, what percentage of your firm's/business unit's overall marketing budget (excluding sales force expenditure) was spent on customer analytics?". 2 Based on "Please describe the performance of your firm's/business unit's marketing group in the following areas relative to your average competitor (consider the immediate past year in responding to these items)". Scale of 1 to 7: 1 = Well below our competition; 4 = About equal; 7 = Well above our competition. Values 6 and 7 are classified as "top tier"; 3, 4, and 5 as "middle tier"; and 1 and 2 as "low tier". SOURCE: McKinsey, DataMatics 2013 Companies with high expenditure on customer analytics perform better in most areas (Exhibit XVI). Contacts 27
- 28. Methodology and sample structure Detailed survey results: Organization and governance Documentation Exhibit XVII Exhibit XVIII SOURCE: McKinsey, DataMatics 2013 1 Based on "The use of customer analytics contributes significantly to our firm's/business unit's performance". "Significant performance contribution through analytics" defined as 6 to 7 on a 7-point scale: 1 = Strongly disagree, 7 = Strongly agree. 2 Based on "To what extent is senior management involved in customer analytics initiatives?". "Extensive involvement of senior management" defined as 6 to 7 on a 7-point scale: 1 = Not at all, 7 = To a great extent. Significant performance contribution of analytics1 Percentage of companies that strongly agree 76 29 +162% Extensive senior management involvement No extensive senior management involvement2 1 Based on "What level of senior management is involved in customer analytics?". Answer categories "CEO" and "Other C-level executives". 2 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor". Aggregate index derived from the dimensions Sales, Sales Growth, Profit, ROI. Comparison of bottom vs. top quartile. 76 45 +69% High performers Low performers2 C-level involvement Companies with C-level executives involved in customer analytics, percent1 SOURCE: McKinsey, DataMatics 2013 High-performing companies are led by data-savvy C-level executives who understand the importance of customer analytics and take a hands- on approach to the topic Organization and governance Having data-savvy C-level executives at the forefront of their customer analytics activities is perceived as key to company performance (Exhibit XVII). Companies that involve their senior management extensively in customer analytics are almost three times as likely to find it makes a significant performance contribution (Exhibit XVIII). Contacts 28
- 29. Methodology and sample structure Detailed survey results Documentation Contacts Jesko Perrey is a Director in McKinsey’s Düsseldorf office. jesko_perrey@mckinsey.com Andrew Pickersgill is a Director in McKinsey’s Toronto office. andrew_pickersgill@mckinsey.com Contacts Lars Fiedler is an Associate Principal in McKinsey’s Hamburg office. lars_fiedler@mckinsey.com Marcus Roth is a Senior Expert in McKinsey’s Chicago office. marcus_roth@mckinsey.com Matthias Kraus is a Practice Specialist in McKinsey’s Munich office. matthias_kraus@mckinsey.com Alec Bokmann is an Expert Associate Principal in McKinsey’s New York office. alec_bokmann@mckinsey.com Julie Hayes is a Practice Manager in McKinsey’s Chicago office. julie_f_hayes@mckinsey.com 29
- 30. Academic advisors Documentation Methodology and sample structure Detailed survey results Contacts/Academic advisors Gary L. Lilien Distinguished Research Professor of Management Science at Penn State and Research Director at the Institute for the Study of Business Markets (ISBM) Frank Germann Assistant Professor of Marketing at the University of Notre Dame 30
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