Vendor Briefing G Pedictive for Bloor Group. March 27, 2013 Björn Goerke Philippe Take

Transcription

1 Vendor Briefing G Pedictive for Bloor Group March 27, 2013 Björn Goerke Philippe Take

2 WE WILL COVER THESE TOPICS Who We Are Lead Scoring App Agenda Customer Benefits Differentiation Business Model Customers Strategy Outlook Product Demo G Predictive Gradient GmbH 2

3 FACTS G PREDICTIVE Company Founder: Björn Goerke, Dr. Dennis Proppe Head office: Hamburg, Germany More than 8 years of experience in Predictive Analytics Mission Providing predictive analytics out-of-the box for everyone ROI-improving solutions based on Predictive Analytics Focus on sales and marketing intensive processes USP Install and you re ready to go No need for systems integration Real-time Predictions-as-a-service and subscription price model G Predictive Gradient GmbH 3

4 TIMELINE 2008 Spin-off from Kiel University Consulting and analytics in data mining Focus on sales and marketing 2012 Decision to become a product company R&D on 4 different products De-marketing of consulting customers First product customers in late The four products are ready for shipping First app in salesforce.com More apps to come Microsoft Navision SAP G Predictive Gradient GmbH 4

5 THE PRODUCTS Lead Scoring Who is going to be my next customer? Next Best Offer What product is my customer going to buy next? Automated Pricing At what price is my customer (still) willing to buy? Predictive Quantities Management How much are my customers going to buy? G Predictive Gradient GmbH 5

6 IDENTIFYING YOUR CUSTOMERS EARLY TAKES YOU TO POLE POSITION G Predictive Gradient GmbH 6

7 LEADS ARE GENERATED THROUGH DIFFERENT CHANNELS Prioritizing correctly Thousands of leads through different channels But who is really interested? Those who identify purchase intent first, will have the best chance to sell G Predictive Gradient GmbH 7

8 THE CHALLENGE: WHO SHOULD I CONTACT - WHO NOT? Starting point Sales pipeline is filled with leads through different channels More leads than one can handle Who should I contact next? And: Who shouldn t I contact? Handling this situation One tries to handle all leads Calls, sales meetings, incentives, etc. Contacting hurts in terms of time and costs The more you invest, the more it hurts Not enough attention for the really important contacts Time is especially consumed by those leads that will not convert Poor chance of response inquiries, improvements, claims, etc. Losing time for leads that will convert to customers G Predictive Gradient GmbH 8

9 CURRENT PRIORITIZATION IN YOUR CRM-SYSTEM How to proceed? CRM-systems provide a compact overview of your leads Typical crtiteria for prioritazation Age Income Marital status Profession Education G Predictive Gradient GmbH 9

10 RULES AND EXPERIENCE BASED RANKINGS ARE LIMITED DUE TO COMPLEXITY Individual criteria do not capture enough influence factors Single criteria and instinct are not sufficient One will miss leads that fall through the grid A lot of potential is dormant on the edge of the grid, because competitors use similar threshold values Combination of criteria is complex Frequently, several criteria are considered together in a subjective assessment But: How should they be evaluated? Which criterion has the highest priority? Human beings cannot consider everything simultaneously Ideally, all crucial factors should be taken into consideration simultaneously One measure to prioritize all leads But how should the criteria be weighted? And how do you know their real impact? G Predictive Gradient GmbH 10

11 G PREDICTIVE OPPORTUNITY MANAGEMENT EVALUATES EVERY LEAD Typical information Customer master data (vague) product interest Gut instinct G Predictive Gradient GmbH 11

12 G PREDICTIVE OPPORTUNITY MANAGEMENT EVALUATES EVERY LEAD Lead Score Is calculated in real-time Is being refreshed dynamically with any change Gets you to the customers who will buy faster G Predictive Gradient GmbH 12

13 LEAD SCORE FOR LEAD RANKING RANK YOUR LEADS BASED ON THE SCORE Ranking The lead score allows ranking of your leads The higher the score, the more likely to convert Concentrate on the leads you want to target G Predictive Gradient GmbH 13

14 BENEFITS OF G PREDICTIVE OPPORTUNITY MANAGEMENT Identification of buyers The Lead Score helps you identifying your cutomers You can focus on the valuable leads Speed Competitors can have the same information Those who contact the TOP leads first, will win the race E.g. after a fair you will be able to contact the most valuable leads immediately Accuracy Concentrate on TOP leads Flop leads cost time and money Shovel flop leads to the back of the pipeline Don t allow your sales department to be blocked by flop leads G Predictive Gradient GmbH 14

15 DIFFERENTIATION OF OUR LEAD SCORING APP No new software The app does analytics and scoring without the customer interacting Automated data preparation Modelling Scoring Save up to USD in initial license fees No integration The is integrated already Seemless work in the well-known software environment No need for the IT department to setup an integration project Save up to 250 days og integration work No wait Install and you re ready to go Leads are scored in real-time Save up to two years before you can go G Predictive Gradient GmbH 15

16 OUR CUSTOMERS G Predictive Gradient GmbH 16

17 BUSINESS MODEL No license fees or upfront cost As-a-Serrvice pricing No initial license fee No integration cost No training cost Just monthly payments 25 per seat per month Minimum: 20 seats Cancel anytime No minimum subscription term Customers can walk away anytime G Predictive Gradient GmbH 17

18 STRATEGY Efficiency leader For our customers Just click and go For us internally: Reduction of operations to the absolute minimum Integrate in platforms We integrate aforehand Customers do not have to integrate themselves As soon as available in the respective AppStore we can sell worldwide Internationalization to begin in 2013 Salesforce.com s AppExchange is the first step into internationalization Present to local sales teams Have the teams multiply in their countries G Predictive Gradient GmbH 18

19 OUTLOOK Integration Salesforce.com Navision SAP Customers 1 new customer per month in new customer per fortnight in new customer per week in 2015 Internationalization UK and France in early 2013 USA in 2013 Rest of Europe in 2014 G Predictive Gradient GmbH 19

20 CONTACT DETAILS Thank you for your attention! Philippe Take Head of Sales Björn Goerke CEO G Predictive Gradient GmbH Gutenbergring 67b Norderstedt pt@gpredictive.de G Predictive Gradient GmbH Gutenbergring 67b Norderstedt bg@gpredictive.de G Predictive Gradient GmbH 20

21 THESE ARE THE DATA YOU NEED Sales success Depending on the product, e.g. Conversion lead to opportunity Conversion oppurtunity to sales Successful upselling event Successful cross-selling event Master Data Customer data Company master data Person master data Product master data, if applicable Customer behaviour, if available The following data can be helpful Purchase history Product usage Previous inquiries Claims, retoures etc. G Predictive Gradient GmbH 21

22 SOFTWARE-AS-A-SERVICE (SaaS) www intranet Encrypted connection > Predictive Engine REST client HTTP client Request: JSON Antwort: JSON Request: HTML Antwort: HTML web server REST-Server Ruby on Rails Passenger/Nginx application server Predictive Engine Encrypted connection > Data base G Predictive Gradient GmbH 22

23 EXAMPLE OF AN API CALL Client sends API-Call to service.gpredictive.de: G PREDICTIVE Web server service.gpredictive.de: Processes the call Forwarded to localhost:8888 on application server through ssh-tunnel: G PREDICTIVE Application server Excutes Knime in local OSGI container pom.zip computes score, which is returned afterwards G PREDICTIVE Web server returns the score to the Client through JSON { lead_id -> ; new_score -> 76 } Duration of this process: ca. 250ms G Predictive Gradient GmbH 23

24 GRADIENT MODEL MANAGER G Predictive Gradient GmbH 24

25 EXTRACT OF OUR METHODS Toolset Most up-to-date methods, e.g. Random Forest neuronal networks Handpicked machine learning methods Self-learning Scores have an error term Every error provides an opportunity for improvment Our methods make use of these opportunities and improve over time Quality criteria The accuracy of the scores can be evaluated before productive use Training and test pertitions of the data Model Fit und quality criteria like ROC-Curve are part of our standard process G Predictive Gradient GmbH 25

26 Success RATIO SUCCESS : EFFORT 100% Base 90% 80% 70% 60% 50% 40% Base 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Sales effort G Predictive Gradient GmbH 26

27 Success LIFT SCORING RULES 100% 90% 80% 70% 60% 50% 40% Base Lift Regeln 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Sales effort G Predictive Gradient GmbH 27

28 Success LIFT PREDICTIVE SCORING 100% 90% 80% 70% 60% 50% 40% 30% Base Lift Scoring Lift Regeln 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Sales effort G Predictive Gradient GmbH 28

29 CUSTOMIZING LEAD SCORES G Predictive Gradient GmbH 29