SALES ANALYTICS. 10 Conversation Starters For Sales Analytics

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Ashley King
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1 SALES ANALYTICS 10 Conversation Starters For Sales Analytics

2 1. What do we mean by Sales Analytics? E-tail CLTV Channel Customer Strategy Segmentation Sales Force structure Touchpoints Strategy Up-sell, Cross-sell Pricing and product mix Feedback and SFA tools Social Media monitoring

3 2. Who to sell to? BIG DATA ANALYSIS MEANS WE CAN REPLACE SEGMENTATIONS WITH 1:1 CUSTOMER RELATIONSHIP, IE: WE MARKET TO A SEGMENT OF 1, WITH THE RIGHT MESSAGE AT THE RIGHT TIME TO THE RIGHT CUSTOMER. - IBM CEO (and President and Chairman) Ginni Rometty

Segmentation: different approaches Range of granularity of customer segmentation approaches Mass markets Segmented markets Micromarkets Individual customer Impact of customer characteristics on the

4 Segmentation: different approaches Range of granularity of customer segmentation approaches Mass markets Segmented markets Micromarkets Individual customer Impact of customer characteristics on the strategy Customer characteristics Impact on strategy Sales/potential/profitability (i.e. customer size) Level of sales and marketing effort Loyalty Mix of sales and marketing activities Buying decisions (centralized, decentralized) Sales process How they want to be serviced Sales team structure Why and when he buys Sales mechanisms Innovator/early adaptor/late adaptor Point in lifecycle when targeted Price sensitivity/need of service and support Product and service offering and value proposition Location of buying Design of sales channels

This understanding leads us to believe that best practices from industry can inform the go forward

maximum impact by finding the right person, at the right time, with the right message, and through the

managing revenue based on extensive Big Data data it collects on its customers Capital One conducts

defection by examining usage patterns and complaints.

5 This understanding leads us to believe that best practices from industry can inform the go forward approach for any client regardless of sector These 1:1 best practices help businesses deliver the maximum impact by finding the right person, at the right time, with the right message, and through the right channels all done in the right way Marriott has developed a system for optimizing room rates and managing revenue based on extensive Big Data data it collects on its customers Capital One conducts more than 30K experiments a year, with varying interest rates, incentives, DM packaging to maximize card sign up rates Snapfish uses churn modeling to accurately manage the customer life cycle and better re-engage customers who are at-risk of switching UPS is able to accurately predict likelihood of defection by examining usage patterns and complaints. A potential defector is then contacted by a sales rep to review and resolve any issues At&t have developed highly sophisticated models to predict cross sell opportunities (e.g. smartphones, U-verse) and tailor special offers to those customers Target uses predictive modeling and trigger modeling to identify new mothers and provide the most relevant offers that suit their needs

1:1 Sales Analytics best practices from Big Data IHG has gathered details, including social media activities, on about 71 million members of its Priority Club rewards program, all newly consolidated

Marketing + Highly Relevant Messaging A model can be used to help aid those targeting/messaging efforts, using it to match the right message to the right person etc.

Who are the most likely to respond and be persuaded by relevant messaging? RIGHT MESSAGE What is the message each key segment should receive based on who they are and why they behave the way they do?

6 1:1 Sales Analytics best practices from Big Data IHG has gathered details, including social media activities, on about 71 million members of its Priority Club rewards program, all newly consolidated in a single marketing data warehouse. This new system has allowed IHG to tailor up to 1500 unique messages, up from 15. Marketing + Highly Relevant Messaging A model can be used to help aid those targeting/messaging efforts, using it to match the right message to the right person etc. eg: Political campaign RIGHT PERSON Who are the voters/ targets that matter the most in terms of consumer profiles and geographies? Who are the most likely to respond and be persuaded by relevant messaging? RIGHT MESSAGE What is the message each key segment should receive based on who they are and why they behave the way they do? What is the message that will resonate the strongest? RIGHT TIMING When should we engage them with the right message? What is the sequence of messages that will be most effective for the run up to an election? RIGHT CHANNEL Through which channels should we communicate the messaging: An ? A door knock? A phone call? Mass media? Or a combination? Product Development + Text Mining Algorithms RIGHT REASON Why do they behave how they do and therefore what is the appropriate message Candidate centric Static issue centric (issue sets that don t change) Dynamic issue centric (e.g, news cycle issues) The 5R Framework lays out a general strategy for how we communicate the message to drive better relevance and win with key audiences Ford used text mining algorithms to analyze over 10,000 mentions and evaluated the potential upside of introducing a 3-blink indicator. Insights from the text mining analytics led to the 3- blink s inclusion in the new 2013 Ford Fiesta. Nice has developed algorithms to listen to customer services conversations in real time and course correct the messaging for the call center rep based on the responses from the customer Marketing + Real Time Messaging Cross sell and up sell Barclays uses analytics to predict who they are most able to up-sell additional products to. They use tools based on who look likes customers who have successfully being up-sold

Acquisition Retention Attrition Not only are members more likely to join in the first months of interaction, but their behavior in the first months of membership is an indicator of future behavior

7 Acquisition Retention Attrition Not only are members more likely to join in the first months of interaction, but their behavior in the first months of membership is an indicator of future behavior Members that frequent the club in the first 90 days are more likely to continue frequenting the club Getting a member engaged early could have positive impact in the long run Average Weekly Check-ins by First 3 Month Check-ins R 2 =.71

This is the famous Big Recent gym project is a great example of customer Data analytics analysis what can Customer database playbook 4 weeks analytics it tell us?

8 This is the famous Big Recent gym project is a great example of customer Data analytics analysis what can Customer database playbook 4 weeks analytics it tell us? - Create a single - CREATE SINGLE DATAMART FROM MULIPLE DATABASES datamart to analyze Play around with the member data and gym establishment as their - Who is likely to churn? nominated home-gym, and say which gyms are doing baldly/ doing well in terms of churn, revenue etc, Top 10, bottom 10 gyms, and why is this? DAYS as a predictor of in-life behavior, predictor of churn, predictor of high value, eg: taking up PT --- CLTV (and ideally that we can apply to our segments), i.e.: who is the valuable clients, can we craft a super simple CLTV model ---- PROFILE based churn models, high engagement, e.g.: tariff bands, gender etc. And related PROFILE cuts, e.g.: profile factors that influence tariff bands chosen TRIGGER based churn model, high engagement e.g.: change of address, not visited in 2 weeks. And some of the interactions between PROFILE and TRIGGERS BEHAVIORS based churn model, high engagement e.g.: freq of gym visit. And do some simple cuts, e.g.: tariff bands and types of engagement; looking at things like attendance of classes etc - What message/offer will stop each churner? - Which recruitment mechanism creates the most valuable customers? - What type of customer ends up being most valuable? - How can behavior in the first 90 days predict in-life behavior, and what can do do to train the customer in this period? - Who can we easiest upsell Personal Training to?

Example Customer Analytics roadmap developed for BT The Data/Analytics/Insights capability assessment provided a diagnostic of the capabilities and opportunities to be developed and implemented.

9 Example Customer Analytics roadmap developed for BT The Data/Analytics/Insights capability assessment provided a diagnostic of the capabilities and opportunities to be developed and implemented. The approach below sets out the roadmap for achieving these new capabilities to drive business impact Diagnostic Roadmap Asses Capability Compare to Leading Practice Identify Improvement Areas Capture the value of closing the gap Soft Determine Approach Develop Assumptions Determine Benefits Determine Costs Calculate Financial Impact Determine Non- Financial Impact Summarize Findings Determine Case for Change Develop Project Charters Prioritise Portfolio Change Manageme nt Conduct assessment and identify gaps Assess project benefits and prioritise (business case) Develop and execute implementation plan Examples of key impacts of the Data/Analytics/Insight roadmap at BT: Customer Segmentation Trigger based marketing to help the Cable TV sales force Churn reduction by predicting the most vulnerable to churn Increased conversion and customer acquisition by making the right offer, and reduced cost of acquisition Propensity modeling to direct offers to the right consumers Increase average revenue per customer Improved up-sell and cross-sell Cohort analysis, and understanding of the first 90 days as a Customer Customer Lifetime Value models to help identify (net) the most valuable customer profiles

10 3. Use and abuse of CLTV models

12 4. E-tail as an example of Sales Analytics E-tail is a great example of some of the data-driven Sales Analytics you can execute

$100.00 $0.00 Pattern 1 Pattern 2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.

3 Snapfish Weekly Performance Dashboard: Summary YOY % W/E 5/28/11 May '11 MTD Q3 - QTD Week MTD QTD Total Net Revenue 2,670,263 11,200,379 11,200,379 18% 2% 2% MVMs

2% 1% 1% Repeat 1,082,194 4,539,137 4,539,137 15% 3% 3% First 100 Days 30,724 133,260 133,260 19% 14% 14% Post 100 Days 1,051,470 4,405,877 4,405,877 15% 3% 3% Actives

800,841-1% -8% -8% Repeat 1,147,146 4,610,121 4,610,121 25% 1% 1% First 100 Days 70,741 263,834 263,834 45% 8% 8% Post 100 Days 1,076,405 4,346,287 4,346,287 24% 0% 0% 0.

1 At Snapfish we conducted various analytics over time to build initial insights and make an impact on the business Step 1 Data Inputs Transaction Data 18 Month Journey to

and Catalogs Web-Site Profiles and Behavioral Data Qualitative Research Customer Service Data Steps 2-3 Diagnostic and Opportunity Prioritization Engagement Patterns

Percentile 95 th Percentile Quantified lost revenues from customer churn and reduced engagement 99 th Percentile 22% 57% 12% 5% 3% 1% 75 th Percentile 15% 37% 35% 7% 5% 1%

27% 35% Transaction Frequency Look-a-like analysis Snapfish Transactors in Last 12 mos B A Precision targeting to enhance product mix Ignore for targeting D Desired

7% 0.5% 77% 77% 75% 44% Identified and quantified the areas of the site where demand falls off Deciles Deciles 1 3 5 10 1 3 5 10 Catalog Analysis B A C EOY D C C Other

13 $ $0.00 Pattern 1 Pattern Net Revenue Snapfish Weekly Performance Dashboard: Summary YOY % W/E 5/28/11 May '11 MTD Q3 - QTD Week MTD QTD Total Net Revenue 2,670,263 11,200,379 11,200,379 18% 2% 2% MVMs 1,144,907 4,843,975 4,843,975 14% 3% 3% Acquisition 62, , ,838-2% 6% 6% First 100 Days 31, , ,724-6% 10% 10% Post 100 Days 31, , ,115 2% 1% 1% Repeat 1,082,194 4,539,137 4,539,137 15% 3% 3% First 100 Days 30, , ,260 19% 14% 14% Post 100 Days 1,051,470 4,405,877 4,405,877 15% 3% 3% Actives 1,525,356 6,356,404 6,356,404 21% 1% 1% Acquisition 378,211 1,746,284 1,746,284 9% 3% 3% First 100 Days 204, , ,443 19% 14% 14% Post 100 Days 173, , ,841-1% -8% -8% Repeat 1,147,146 4,610,121 4,610,121 25% 1% 1% First 100 Days 70, , ,834 45% 8% 8% Post 100 Days 1,076,405 4,346,287 4,346,287 24% 0% 0% At Snapfish we conducted various analytics over time to build initial insights and make an impact on the business Step 1 Data Inputs Transaction Data 18 Month Journey to Uncover & Activate Insights First 6 months Next 6 months Last 6 months Primary Research: A&U Study Neuroscience Research 3 rd Party Demographic and Lifestyle Data and Catalogs Web-Site Profiles and Behavioral Data Qualitative Research Customer Service Data Steps 2-3 Diagnostic and Opportunity Prioritization Engagement Patterns Identified the differential value of customers across their lifecycle Beginning Period Level of Engagement Bottom 75% Churned Customers Bottom 75% 75 th Percentile 90 th Percentile 95 th Percentile Quantified lost revenues from customer churn and reduced engagement 99 th Percentile 22% 57% 12% 5% 3% 1% 75 th Percentile 15% 37% 35% 7% 5% 1% 90 th Percentile 95 th Percentile 99 th Percentile Migration and Churn Matrix Ending Period Level of Engagement 12% 29% 23% 23% 11% 2% 9% 20% 19% 16% 30% 6% 5% 12% 11% 10% 27% 35% Transaction Frequency Look-a-like analysis Snapfish Transactors in Last 12 mos B A Precision targeting to enhance product mix Ignore for targeting D Desired behavior: Heavy concentration of MVM C Precision targeting to increase frequency Churn Modeling AOV Identified high Identified the drivers of potential customers high value customer that profile like top churn customers Click stream analysis Total Site Visits Main Page 1 Page 2 Page 3 Page 4 Page 4 Transaction 2.4% 2.2% 1.7% 0.7% 0.5% 30M 18.3% 77% 91% 75% 44% 18.3% Path 1 Path 2 14% 2.4% 1.7% 0.7% 0.5% 77% 77% 75% 44% Identified and quantified the areas of the site where demand falls off Deciles Deciles Catalog Analysis B A C EOY D C C Other Rationalized and optimized catalog mailing lists Steps 4-5 Overhaul design language End unprofitable affiliate partnerships Win-Back Program Dashboard Web-site Development Roadmap Q1 Q2 Q3 Q4 Example Initiatives informed by analytic insights Refreshed all creative assets to reflect a more upscale brand experience, tied to priority customer profiles Eliminated and renegotiated affiliates and partnerships that brought in low quality customers Details Confidential Details Confidential Details Confidential Details Confidential Developed a program to reactivate lapsed customers that included a surprise and delight program Designed and helped implement a customer dashboard that tracked acquisition, retention, and churn 1. Initiative #1 Tactic 1 Tactic 2 2. Initiative #2 Tactic 3 Tactic 4 Tactic 5 Facilitated a cross-functional ideation session to develop solutions to web-site bottlenecks and captured the ideas in a 12 month development roadmap Drove double-digit growth in profitable revenues

14 5. Sales Force What is the right size and structure of the Sales Force in a multi-platform (Sales Reps, KAMs, web ordering, call center) and multi-channel (Resellers, partners, wholesalers) environment?

Prophet_Rep Mix Sales Force size/ structure/ allocation of effort/ territory design Prophet-RepMix comprises a number of

resources across channels and product mix at a strategic (ie: national) level, and then territory design - Sizing of a

15 Prophet_Rep Mix Sales Force size/ structure/ allocation of effort/ territory design Prophet-RepMix comprises a number of elements in the model: Channels Segmentation Audience at each channel Call frequency Different rep types Mix of reps & other service mechanism (e.g. internet, call centers) - Mix of rep and distributors depending on channel mix And used for: - Optimization of sales resources across channels and product mix at a strategic (ie: national) level, and then territory design - Sizing of a sale force - Sensitivity analysis depending on channel mix. Allows us to cost rep allocations and structures against channels, and to wargame scenarios - Helps us build a salesforce bottom up and then optimize it

16 6. Social Media Tools What should your Sales Force be talking about?

19 7. SKU Mix/ Price/ Promo What is the optimized product mix at the right price?

20 We see an opportunity to improve profitability and increase revenues by optimizing SKU mix, price and promotion together These interrelated decisions must be considered as part of an overall system and addressed by holistically Objective: Shift from liquidationdriven to strategic promotion, and distinct use of planned Promotion, Markdown glidepaths, and Sale constructs. Promotion, Markdown, and Sale are very different things! Promotions SKU mix Pricing Pricing/Promo SKU Mix Process Objective: Optimized / reduced SKU mix - Minimize cannibalization - Flex by Season - Flex by Store type - Create SKU mix that covers a grid of attributes and price points efficiently Objective: Prices that reflect the value proposition and are optimized to the new SKU assortment, ie: a Price Ladder Objective: Create an appropriate and right-sized go forward process at for SKU, pricing, promo

21 It is very common to see that the last 20% of SKUs are actually destroying overall profitability by cannibalizing higher margin SKUs Generally, SKU assortment projects cut 20% of SKUs to optimize overall margin. Moreover, the structure of the SKU mix is optimized so it best dovetails with a coherent price ladder, and can lend itself optimally to planned promotion, markdown glide paths and sales constructs. 100% 75% Volume Revenue Margin Illustrative In this example, approx the last 35% of SKUs do not grow Revenue or Margin, and the last 20% of SKUs are actually destroying Margin 50% 25% 0% 0% 20% 40% 60% 80% 100% % SKUs ranked by incremental Margin Sometimes, there are assortment reasons to size a SKU mix beyond a point of zero incremental Sales, but NOT beyond a point where margin starts to be destroyed

22 It is worth noting that a 5% increase in Price can often drive a 20% increase It is very common in Retailer to see gross that margins the last 20% of SKUs are actually The destroying last 1/3 overall of SKUs profitability do not add by cannibalizing any incremental higher sales margin SKUs The last 20% of SKUs typically destroy Margin Generally, SKU assortment projects cut 20% of SKUs to optimize overall margin. It gives highly sub-optimal answers to optimize Sku mix, pricing, Moreover, the structure of the SKU mix is optimized so it best dovetails with a and promo separately. They must be optimized together coherent price ladder, and can lend itself optimally to planned promotion, Optimizing markdown glide SKU/ paths Price/ and sales Promo constructs. together can frequently drive a 30% increase in gross margin for a retailer Illustrative Many retailers Volume do not have Revenue the clarity Margin of the fundamentals of: 1) Creating 100% a SKU mix that covers white space efficiently last 35% of SKUs with do a not corresponding Price Ladder to match and promotion grow Revenue planned; or Margin, and the last 20% of SKUs 2) There is a profound difference between Pricing, Planned- 75% Promotion, Mark-down, and a Sale In this example, approx the are actually destroying Margin 50% 25% 0% 0% 20% 40% 60% 80% 100% % SKUs ranked by incremental Margin Sometimes, there are assortment reasons to size a SKU mix beyond a point of zero incremental Sales, but NOT beyond a point where margin starts to be destroyed

23 For example Price and SKU in a different context T-Mobile reconfigured its offers to create a coherent set of product configurations and prices to maximize acquisition, RPU and up-sell/cross-sell while reducing churn. Far fewer SKUs at clearer and more parsed price points CORE PLAN CONFIGURATION TARGETED VARIATIONS OF PLAN

24 8. Loyalty Loyalty means a lot more than loyalty schemes

25 We have successfully designed and developed large loyalty programs Used a combination of creative techniques and quantitative research to create a differentiated and competitive loyalty program anchored on service rather than points that is driving profitable revenue and share of wallet among target segments Used customer research and ideation sessions to develop a customer-centric loyalty program targeting the highest value customers with a breakthrough service experience Collaborative ideation and co-creation sessions, quantitative customer research, and end-to-end visual design and identity creation drove development of a distinctive loyalty membership program that became an integral part of the brand International Casino and Resort Quantitative insights and in-depth customer research were used to identify and prioritize the most important touchpoints of the customer experience and design a loyalty program which reflected target customers desire for fun, freedom and choice

26 A loyalty program is just one aspect of a loyalty mindset. Loyalty is another word for share of wallet. Share of wallet is achieved by created the value prop (including a loyalty program) for a particular segment BRAND Differentiated Value Proposition Segmentation - Targeting Customers Customer Experience Loyalty Programs Having a compelling branded value proposition to target customers - Product - Service - Price - Channel - Experience Determining customer economics - Revenue - Profitability - CLTV - Usage vs. spend CRM & capturing and mining customer data Segment of 1 Managing customer relationship across touchpoints and patterns of engagement Building on the brand, value proposition, segments and customer experience - Recognition - Access - Shared expertise / knowledge - Incentives - Rewards LOYALTY & VALUE CREATION

27 9. Cross-Sell, Up-sell The #1 task of Sales is to increase Share of Wallet by up-selling

Cross/multiline selling in action Barclays Bank cross-sell pathways Acquisition is typically via credit cards Identify

into Checking accounts Use spending pattern analysis to determine best prospects for Wealth up-sell Call-center scripting

offer, best media of delivery Cross-sell into Wealth Management Cross-sell of additional products based on next offer

28 Cross/multiline selling in action Barclays Bank cross-sell pathways Acquisition is typically via credit cards Identify prospects to cross-sell Right offer, eg: in terms of account specification, first year offer Right message/ channel Cross-sell into Checking accounts Use spending pattern analysis to determine best prospects for Wealth up-sell Call-center scripting In-branch flagging of high value customer, and invited to meet a Wealth Manager in-branch Decision rules for what product to offer, best media of delivery Cross-sell into Wealth Management Cross-sell of additional products based on next offer propensity models that track typical pathways if buy product x, product y is next best offer Continuous learning algorithms Test and Learn programs

29 For example: Here we are at State Farm Conduct cohort analysis of current SF members to identify pathways to multi-product, multi-line ownership. What are the journeys though the portfolio? Key questions Where do SF members enter the SF portfolio? What happens after they enter the portfolio? Buy a new product Buy new offerings within a single product Remain static How can we get static SF members to behave like other similar members who buy new products or new offerings? Engagement $1,800 Patterns, Customer Cohort Segmentation Multiline Life Insurance Cross Sell Auto Total Monthly Premium $1,600 $1,400 $1,200 $1,000 $800 $600 $400 $200 $0 Group 1 Group 2 Group 3 Multiline Life Insurance Group 4 Cross Sell Auto Year 1 Year 3 Year 5

30 10. Touchpoint Analysis Thinking about the Sales and Experience cycle, what are all the touchpoints that a customer encounters?

32 Thank You For Listening And Contributing! Any questions?

33 For more information contact: James Walker Senior Partner Tel: Mob:

Value Proposition for Financial Institutions

WWW.CUSTOMERS-DNA.COM VALUE PROPOSITION FINANCIAL INSTITUTIONS Value Proposition for Financial Institutions Customer Intelligence in Banking CRM & CUSTOMER INTELLIGENCE SERVICES INFO@CUSTOMERS-DNA.COM