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Smarter Customer Analytics - Customer DNA
This document summarizes a case study of how a major US retailer used customer analytics to optimize their marketing budget. It involved (1) building a data-driven customer profile, (2) applying customer insights to optimize spending across channels, regions, and customers. Advanced customer clustering analyzed over 30 variables to segment customers into 12 highly differentiated groups with tailored marketing strategies, such as a "Brand Fanatics" group that represented 9% of customers but 30% of revenue.
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Smarter Customer Analytics - Customer DNA
- 1. “Know me” -Getting Closer to Your Customers through Applied Analytics Mark Matiszik © 2012 IBM Corporation
- 2. Know, Listen To,and Empower Me Being treated as an individual has moved from a Desire to an Expectation © 2012 IBM Corporation
- 3. Case Study: Howcan “Customer Centricity” really help solve a business challenge? Major US Retailer’s Big Unanswered Question: “How do I eliminate unnecessary spend from my Marketing budget?” Step 1: Build a data-driven Lens of the Customer Step 2: Apply that Customer Foundation as the key input to Optimizing between Channels/Regions/Customers © 2012 IBM Corporation
- 4. Balancing on aThousand Curves The picture isn’t simple – there are many customers, and many media types. With two customer groups, for instance, we have two curves… Customer 1 Customer 2 D 3 E Customer B 2 Spend C 1 A TV Spend But, how do we know what is optimal for each customer? © 2012 IBM Corporation
- 5. Acting on CustomerInsight The customer’s voting record – the digital footprints of their countless decisions – has the power to tell us who they are, and what matters to them. Analytics Technology Business Integration © 2012 IBM Corporation
- 6. The New Eraof Customer Understanding and Segmentation The key to achieving the high ROI and Profit potential of multi-channel shopping is advanced customer analytics Traditional Approach Advanced Clustering Models based on few dimensions Models based on many dimensions – demographics, value, or basket of customer behavior Customer Value You Are Demographic What You Buy Preferred Product Categories Sales Latest & Greatest Income Length of Time Preferred Channel as Customer Price Focused Participation in Loyalty Program Recency + Frequency Value Maximizers + Value Use of In-House Connected Convenience Response to Media Credit Card Use of Service Programs Time until Repurchase in Key Categories Return/Exchange Behavior Breadth of Trans- Basket Geo- Categories Shopped actions Analysis graphy Highly actionable clusters are based on Typically not actionable because the customer’s response to various customers are more complex than 2 or dimensions of the Retailer’s value 3 dimensions proposition © 2012 IBM Corporation
- 7. No Guessing: AnalyticsCan Reveal Who Your Customers are Begin with 30-40+ Modeled Variables from Customers’ Digital Footprints Each Variable is like a gene, which describes a facet of customer behavior Useful on their own, but also provide the input for Clustering Age + Income + Geography Most segmentation approaches CTP Customer Annual Transactions only focus here: Annual Spend Level Use of In-House Credit Card Econometric: Real-estate & Unemployment Facebook Page Engagement Gift Registry User Return / Exchange Behavior Preferred Product Categories Breadth of Categories Shopped Modeled time to next purchase Response to Media Length of Time as Customer Recency + Frequency + Value © 2012 IBM Corporation
- 8. Clusters are Basedon the most significant Modeled Variables Revolutionary customer segmentation approach tailored uniquely to each client’s business model, customer data and operational practices, yielding highly actionable customer groups Preferred Product Categories Length of Time Preferred Channel as Customer Participation in Recency + Frequency Loyalty Program + Value Use of In-House Credit Card Response to Media Use of Service Programs Time until Repurchase in Key Categories Return/Exchange Behavior Breadth of Categories Shopped Action Clusters are Highly homogeneous – it is difficult to get into a cluster based on 10+ dimensions, ensuring that the customers are very similar to one another Highly differentiated – the process ensures as much “distance” between clusters as possible © 2012 IBM Corporation
- 9. Sample Clusters Rank Action Cluster % of Customers % of Spend 1 Brand Fanatics 9% 30% 2 Core Customers 8% 18% 3 Online Socialites 6% 14% 4 Hurt by the Economy 8% 8% 5 Potential Pool 7% 7% 6 Make it Interesting! 6% 6% 7 Let’s Bargain 10% 3% 8 Find me Online 7% 2% 9 Unengaged 17% 7% 10 Luxury for Me 4% 2% 11 Until Next Year (One and Done!) 13% 2% 12 Just Window Shopping 5% 1% © 2012 IBM Corporation
- 10. This is theOutcome Example: “Brand Fanatics” Vital Statistics Strongest Loyalty: Over 85% are part of loyalty program 89% have shopped over 5 categories 91% have been customers for 7+ years Almost no new customers in <3 years 70% are due to purchase within 60 days 60% are using a private label credit card, 30% exclusively for all purchases Highest Return on Marketing scores Marketing Call to Action – RMI 37:1 9% of customers 30% revenue EMOTIONAL BENEFIT: Sports enthusiast BRAND PROMISE: Latest & Greatest, Multiple Sports Category Breadth and Depth CUSTOMER AWARENESS: Loyalty promo, new product releases, direct mail and email TOUCH POINTS: Multi-Channel, In-store and on web UNIQUE IDEA: ‘Co-Branded Credit Card Promotion’ PRE-STORE: Mobile, Blogs, Social Networks IN-STORE: Mobile applications and shopping aids, services merchandise together POST-STORE: Online, loyalty program mailings and emails © 2012 IBM Corporation
- 11. Clusters can deepenInsights from Existing Segmentations © 2012 IBM Corporation
- 12. Next Step: Optimization Usethe lens of the Customer Foundation as a Primary Input for solving the most difficult challenges within the business Analytics Technology Business Integration © 2012 IBM Corporation
- 13. Marketing Media Optimization– with a Customer Lens • Industry data • Systematic risk Economy • Demand forecast Optimization • Transaction data • Modeled Variables Customer Behaviors • Action clusters • Multi-objectives • Performance data Media • Policy constraints • Saturation • Optimal decisions • Action Exposures Analysis © 2012 IBM Corporation
- 14. Case Study 1:Enterprise Marketing Media Mix Optimization Challenges & Background Solution Optimally invest a $MMM+ advertising Established customer foundation through budget to maximize sales AND unique customer segmentation approach maintain/reduce market spend? Enabled prescriptive media mix How do I apply my knowledge of my optimization engine for Marketing customers to determine the proper investment against the Clusters proportion of investment in each marketing Developed solution enabling ‘what if’ type? scenarios and returning ‘what’s best’ output Benefits and Results Reduced saturation of budget 5-7% (~$50M) Outperformed industry with greatly reduced budget; identified $1B in additional revenue Improved conversion and engagement © 2012 IBM Corporation
- 15. Case Study 2:Re-engage ‘Lapsed Best Customers’ to Drive Revenue Challenges & Background Solution How best to reactivate lapsed ‘best Developed Behavioral Models to enrich customers’ in loyalty program? understanding of customers: Leveraged customer foundation to Tailor copy, creative and offer inform media preference, creative based on customer preferences personality, and communication timing Minimize execution costs by identifying Selected customer list, designed 5 communication channels each creative versions and delivered through customer would responsive most to preferred marketing channel Benefits and Results Response Rate 12% Offer 1 Offer 2 11,69% ~100% and ~200% increased response 10,43% 10% 8,70% rate over expected results, for two 8% 8,67% tested campaigns 7,81% 6% 4,91% 4% 6,59% Reactivated customers drove 2% 0,59% 3,80% Expected Response $180/customer incremental revenue in 0% 0,37% 2 months 28-Nov 5-Dec 12-Dec 19-Dec 26-Dec © 2012 IBM Corporation
- 16. Operationalizing Action ClustersAcross the Organization Marketing Who? “What messages are Customer relevant to my targeted customers and how and when do I communicate with What? them?”” Product How? Merchants Channel “Who is in the market, what do they want and how do I When? inspire them to purchase?”” Lifecycle Mgmt. © 2012 IBM Corporation 4/25/2012
- 17. Final Step: Operationalizethe Insights (and Repeat) With the tools and capabilities, the organization begins to make better decisions that no longer treat all customers alike. Analytics Technology Business Integration © 2012 IBM Corporation
- 18. Break-out session Round2 Please join us during the break-out session Get Personal! Het belang van personalised promotions voor retailers Hoe kunt u dit realiseren binnen uw organisatie? Mark Matiszik, Associate Partner, Retail Center of Competence, IBM Ewald Hoppen, Team Lead Web Analytics / Senior Web Analyst, Wehkamp.nl © 2012 IBM Corporation
- 19. Thank You! Mark Matiszik IBMRetail Center of Competence @MarkMatiszik © 2012 IBM Corporation