Uploaded byMcKinsey on Marketing & Sales
Are your digital channels driving growth?
The document discusses the evolving role of digital channels in consumer decision-making, emphasizing the integration of mobile technology in shopping behaviors. It highlights the importance of leveraging data to create personalized customer experiences and to improve marketing strategies for customer acquisition and retention. Additionally, it notes a significant talent gap in deep analytical skills required for digital marketing success as organizations adapt to a more data-driven environment.
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Are your digital channels driving growth?
- 1. WORKING DRAFT Last Modified3/19/2014 3:09 PM Eastern Standard Time Printed Using Digital Channels to Grow McKinsey on Marketing & Sales – Slideshare Brief June 28, 2012 Any use of this material without specific permission of McKinsey & Company is strictly prohibited
- 2. McKinsey & Company LastModified3/19/20143:09PMEasternStandardTimePrinted |1 Consumers increasingly use mobile in everyday search-shop-buy decisions In-store, compare prices 44% 9%In-store, make purchases on my phone Seek input on purchase before shopping 15% Collect rewards from checking in to stores 16% "Follow" my favorite stores and merchandisers 21% Check local store stocks before shopping 25% Shop directly on my phone at retailer websites 27% In-store, research products 35% Inform purchase decisions using consumer ratings 37% Research products before store visits 41% SOURCE: McKinsey Mobile Payments Global Survey Key Activities Respondents who use mobile device for activity 1 n=1000; Question: “How do you use your mobile device when shopping (select “yes” if you do the following activity)?”
- 3. McKinsey & Company LastModified3/19/20143:09PMEasternStandardTimePrinted |2 Evaluate Buy Experience Bond Advocate Consider Evaluate Buy Experience Bond Advocate Consider Digital is a way to accelerate the Consumer Decision Journey
- 4. McKinsey & Company LastModified3/19/20143:09PMEasternStandardTimePrinted |3 Managing throughout the Customer Decision Journey requires data, content, and programs at each step ▪ Recognize and provide top users with rewards, services ▪ Build lifestyle events to make card indispensable ▪ Use digital data to more precisely target customers for credit card offers ▪ Tailor marketing and product design to facilitate consideration and application ▪ Design quick and easy comparison tools, building online experience early ▪ Use cookie data to understand customer pathways ▪ Adjust online offer based on available customer information ▪ Streamline decisioning for „instant‟ use of card online ▪ Build online usage immediately and set expectations for servicing with digital welcome kit ▪ Use high-touch outreach in digital channels in first 90 days ▪ Use digital to incent purchases, especially recurring payments ▪ Leverage PFM tools to make card relationship „at the center‟ of financial toolkit ▪ Track spending behavior and provide updates on rewards, offers, and features/benefits ▪ Encourage referrals and additional authorized users ▪ Make it easy for customers to link to the card, and post about their experiences Acquire and on-board customers Build usage and ‘first in wallet’ status Drive loyalty, referrals, and cross-sell Evaluate Buy Experience Bond Advocate Consider CREDIT CARD EXAMPLE
- 5. McKinsey & Company LastModified3/19/20143:09PMEasternStandardTimePrinted |4 Negative buzz reaches far. Social media had similar influence as multi- million dollar investment in TV Breakdown of drivers of customer acquisitions By marketing activity, percent Neg. social media -7.8 Search 9.0 Display 8.4 Affiliate 7.3 Print Special 8.3 Print General 3.6 TV 8.5 Base, incl. Price DISGUISED TELECOM CLIENT EXAMPLE SOURCE: Digital Marketing ROI-Team
- 6. McKinsey & Company LastModified3/19/20143:09PMEasternStandardTimePrinted |5 Data can power creation of customized offers and experiences 25 (0.3) 12 (1.0) 9 (1.5) 4 (1.0) 9 3.0 9 0.2 5 3 (1.6) 8 1.2 4 0.9 3 0.1 2 (1.7) 3 1.0 2 0.3 1 0.6 1 (0.4) Multiple visits, multiple categories Single visits, mult iple categories FY10 % of cust- omers $GM impact, bn Multiple visits, single category Using migration matrix allows leveraging past information about customer preferences and behaviors to drive customized offers aimed at bringing them back to the more profitable segment This situation is analogous to introducing an affluent customer to a Financial Advisor Customer shows behavior indicating high potential… …but stops purchases Store entices customer to come back to the store by assigning a personal stylist After receiving a personal touchpoint, the customer continues as a loyal client Single visits, single category Multiple visits, multi ple categories Single visits, multi ple categories Multiple visits, single category Single visits, single category Send offers driving store visits Send offers driving aisle crossing Upscale department store Upscale retailer uses data to shape customer experiences for affluent customers – here a migration matrix focusing on high value movements FY11 Focus on high potential early tenure customers and lock them in by assigning a personal stylist
- 7. McKinsey & Company LastModified3/19/20143:09PMEasternStandardTimePrinted |6 Different Big Data levers have varied impacts on operating margin -30 -20 -10 0 10 20 30 40 50 Price transparency Supply chain Operations Merchandising Marketing SOURCE: Expert interviews; publicly available data; McKinsey case studies; McKinsey Global Institute analysis Impact on operating margin %
- 8. McKinsey & Company LastModified3/19/20143:09PMEasternStandardTimePrinted |7 Example: Margin improvement via digital marketing EngagementTraffic Total + $200 million Conversion ▪ Organic search ▪ Paid search ▪ Online media targeting ▪ Site cross-sell ▪ Email response ▪ Direct mail reduction ▪ Streamline content ▪ Call volume reduction ▪ Conversion path ▪ Card pre-approval ▪ Application design ▪ New offers Margin improvement ENGAGEMENT EXAMPLE
- 9. McKinsey & Company LastModified3/19/20143:09PMEasternStandardTimePrinted |8 Big Data companies have outperformed their respective markets and have created competitive advantage SOURCE: Bloomberg and Datastream; annual reports; McKinsey CPAT tool; McKinsey analysis Percent Revenue 1999-2009 10-year CAGR EBITDA 1999-2009 10-year CAGR Other competitors Big Data leaders Grocers Online retailers Big box retailers Casinos Credit cards Insurance 9 14 11 9 24 12 6 8 9 5 5 -1 14 9 12 10 22 11 2 -1 5 1 -15 3
- 10. McKinsey & Company LastModified3/19/20143:09PMEasternStandardTimePrinted |9 300 30180 150 2018 projected demand 440–490 Talent gap2008 employment Others1Graduates with deep analytical talent 140–190 2018 supply SOURCE: US Bureau of Labor Statistics; US Census; Dun & Bradstreet; company interviews; McKinsey Global Institute analysis Demand for deep analytical talent in the US could be 50-60% greater than its projected supply by 2018 50–60% gap relative to 2018 supply 1 Other supply drivers include attrition (-), immigration (+), and reemploying previously unemployed deep analytical talent (+) Supply and demand of deep analytical talent by 2018 Thousand people
- 11. McKinsey & Company LastModified3/19/20143:09PMEasternStandardTimePrinted |10 Final perspective Digital is now the center of gravity for marketing and customer engagement Increasing consumer engagement digitally has impact to topline and margin Moving at ‘digital speed’ requires new capabilities, skills, and mindset