Rifle Methodology Applied For KV Site2

Slide1: 

Project Rifle Improving targeting with meaningful quantitative analysis Note: this is a subset of a longer presentation. Thank you to Bill Campbell & Tellme Networks for their fundamental contributions.

Inspiration : 

Inspiration “If you don’t have any facts, we’ll just use my opinion.” Jim Barksdale “If we make the wrong decision, it’s your fault.” Bill Campbell

What is Rifle?: 

What is Rifle? Quantified decision making framework Provides a rigorous means of prioritizing among similar options Done well, leads to improved decisions that are broadly supported Appropriate for major decisions In general involves 100+ hours of work & expensive resources per use

Rifle Improves Do-Or-Die Market Decisions : 

Rifle Improves Do-Or-Die Market Decisions “Market Rifle” – what market segments should we be in? Intuition: focus on top 2,000 accounts; expand to complementary markets Rifle analysis: narrow focus to 30 of top 200; stay in core market for 2+ years “Account Rifle” – which accounts should we target? Intuition: great financial services targets: [Company 1], [Company 2] Rifle analysis: none of the above are appropriate: better are [Com. 3], [Com. 4] “Channel Rifle” – what channel strategy, what partners? Intuition: leverage large existing sales forces of channel partners to expand sales Rifle analysis: direct sales assisted by partners much more likely to succeed

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Rifle In Action: Market Strategy

Rifle In Practice: Market Strategy Analysis: 

Rifle In Practice: Market Strategy Analysis Establish clear alternatives Establish targeting & prioritization framework Get buy-in on the framework; know how it will be applied Execute the analysis … and don’t screw it up!

Targeting the Right Market Segments:Market Rifle Methodology Summary: 

Targeting the Right Market Segments: Market Rifle Methodology Summary Goal #1: market leadership Goal #2: expand in a disciplined, highly leveraged way Goal #3: identify & exploit network effects Detailed Framework

Quantitative Market Comparison: “Rifle-izing” Key Characteristics: 

Quantitative Market Comparison: “Rifle-izing” Key Characteristics Evaluate and score each market on these 6 characteristics Use simple 0-5 scale with typical “s-curve” value function for all characteristics (except “market size”, which is in $Bn)

Expansion Strategy:Market Rifle Given Current Data: 

Expansion Strategy: Market Rifle Given Current Data Market Focus << Back

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Rifle In Action: Financial Services Segmentation & Targeting

Financial Services Market Analysis: Huge Opportunity … Where to Focus?: 

Financial Services Market Analysis: Huge Opportunity … Where to Focus? Total Market Potential ~$525M / yr

Rifle In Practice: Financial Services Market Analysis: 

Rifle In Practice: Financial Services Market Analysis Establish clear alternatives Which verticals (brokerage or credit cards?)…which accounts? Establish targeting & prioritization framework 13 carefully selected criteria based on win-loss analysis, customer interviews… Thoughtful weighting is as important as the criteria Get buy-in on the framework; know how it will be applied Get buy-in on the framework before doing the analysis Application: A strategy presentation? Or ongoing process? Execute the analysis … and don’t screw it up! Incorrect or misleading information is worse than nothing Getting good data requires creativity

Financial Services Market Analysis: Huge Opportunity … Where to Focus?: 

Financial Services Market Analysis: Huge Opportunity … Where to Focus? Total Market Potential ~$525M / yr

Total Market Potential by Vertical:Retail & Credit Cards Surprisingly Large: 

Total Market Potential by Vertical: Retail & Credit Cards Surprisingly Large Retail Banking ~$180M / yr 34% 7% Credit and Charge Cards ~$215M / yr 41% 5% Mortgage Credit ~$25M / yrs Retail Brokerage ~$40M / yr Investment Services ~$35M / yr 7% 6% Life Insurance / Diversified Financial Services ~$30M / yr

Financial Services After Rifle Criteria:Target Universe Sharpens Considerably: 

Financial Services After Rifle Criteria: Target Universe Sharpens Considerably Too Small (3%) Recent IVR Purchase (39%) Other Telecom Network (42%) “Rifle” Retail Banking (14%) Recent IVR Purchase (15%) Other Telecom Network (41%) “Rifle” Credit and Charge Card (40%) OTN (36%) 14% Too Small (46%) OTN (11%) Recent IVR Purchase (50%) “Rifle” RB (17%) “Rifle” Investment Services (73%) OTN (10%)c Other (9%) Small (9%) “Rifle” Life Insurance / Diversified Financial Services (91%) Retail Banking Credit and Charge Cards Mort. Credit Retail Brok. Inv. Svcs Life Ins. / Div. FS Too Small (4%) Small (9%) 34% 7% 41% 5% 7% 6%

Post-Rifle: Account Priority Becomes Clear: 

Company 17 (18%) Company 12 (7%) Company 11 (4%) Company 13 (8%) Company 14 (10%) Company 15 (11%) Company 16 (14%) Company 18 (30%) Post-Rifle: Account Priority Becomes Clear Company 1 (5%) Company A (3%) Company 2 (5%) Company 3 (6%) Company 4 (6%) Company 5 (10%) Company 6 (13%) Company 7 (35%) Company B (14%) Company C (16%) Company D (16%) Company E (22%) Company F (30%) Com. AA (7%) Company BB (9%) Company CC (10%) Prudential (11%) 14% Company (18%) Company AA (45%) 8% 47% 14% 17% Company #1 (50%) Company #2 (17%) Com. #3 (17%) Retail Banking Credit and Charge Cards Retail Brokerage Investment Services Life Insurance / Diversified Fin Svcs

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Backup Slides

Rifle In Practice: Get Meaningful Data: 

Rifle In Practice: Get Meaningful Data Incorrect or misleading information is worse than nothing Don’t trade precision for correctness Analyst numbers can be useful, but often are wildly off Consider weighting data by confidence in source Don’t be seduced by large sample sizes 20 random & representative inputs beats 2,000 biased ones Getting good data requires creativity & persistence Extrapolate from high confidence, easy to find data Example: revenue opportunity by company Be creative in where to get the data Customers, potential customers, employees, former employees, advisors – network, network (remember, sometimes just 20 high quality responses are meaningful) Make it as easy as possible to gather clean data Beware leading questions, decouple weightings from data gathering Back