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ATi Customer Value Management

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Susan Cordts, President/CEO of Adaptive Technologies, Inc. (ATi) provided this presentation to attendees of the American Marketing Association Phoenix meeting on August 27, 2008. The presentation …

Susan Cordts, President/CEO of Adaptive Technologies, Inc. (ATi) provided this presentation to attendees of the American Marketing Association Phoenix meeting on August 27, 2008. The presentation details analytics, customer values and how to target the right customer, at the right time, with the right message and media.

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  • 1. Customer Value Management Adaptive Technologies, Inc. (ATi) PRESENTED TO: The American Marketing Association - Phoenix August 27, 2008
  • 2. Presenter: Susan Cordts, President / CEO
    • Recognized leader of growth-oriented and developing companies.
    • Primarily focuses on ATi’s strategic and client relationships.
    • Expert in creating solutions in a variety of industries using predictive analytics and advanced technology.
    • Masters in International management (MIM) – Thunderbird, The Garvin School of International Management.
    • Adaptive Technologies, Inc. (ATi)
    • 4530 East Shea Boulevard, Suite 130
    • Phoenix, Arizona 85028
    • 602-923-4200
    • [email_address]
  • 3.
    • Marketing’s Challenge
    • Customer Value Management
      • Results
      • Process
      • Deployment for success
    • Business Intelligence vs. Predictive Modeling
    • Conclusions
    Agenda
  • 4. Daily Challenge
    • To reach our “targets” for the month, quarter, year
      • Revenues
      • Profits
      • Leads
      • Net new customers
      • Product sales
  • 5. Meeting the Challenge
    • Focus on acquiring new customers
    • Increasing sales with current customers
    • Retaining loyal customers
    • Identifying high revenue customers
    • Focus on customers who are highly profitable
    • Customers in a given segment or product line
    • Segment our customers a number of ways to be able to work with them in a number of venues
  • 6. Effective?
    • Are we doing this in the most efficient and effective way?
    • Do we really understand our customers’ potential values through our current methodologies?
    • Are we looking forward or looking at the past in these processes?
    • How certain are you that you will reach your targets?
  • 7. Organizational Data
    • Collect large volumes of data
      • In multiple silos within the company
      • Difficult to access and/or analyze it
    • Use portions of it to drive decisions via reports
      • Multiple reports for various departments and views of the organization
      • Different cuts of the data are used to tell different stories in different scenarios
  • 8. Customer Value Management
    • A methodology for looking at your customers in a whole new way
      • Let data tell the story
      • Based on how they meet your organizations’ goals
      • Leveraging data to paint a colored photo of them rather than a stick man image
      • Ability to put them under any and all appropriate boxes
        • No need to think of them uni-dimensionally
  • 9. Goals First
    • Focus on the portfolio of customers to meet your organization’s goals
    • Maximizing Customer Lifetime Value
      • Gaining the customer is only half the battle
      • Varies for individual customer
      • Goal is to be able to engage with the customer in such a way as to achieve her maximum potential
  • 10. Results
    • Reach the right customer at the right time with the right offer via the right media
    • Efficiently and effectively reach your goals through the appropriate mix of quality and quantity
  • 11. Criteria to Consider
    • ROI over the next 3 years will be greater than the investment
    • Volume of customers
    • Growth plans and strategies
    • Availability of data
    • Willingness to “do” something different based on the results
    • Openness to hearing something different than your current intuition
      • Forgoing intuition when the data doesn’t support it
  • 12. How Do You Do It?
    • Integrate data
      • Customer demographic data
      • Sales or transactional data
      • Append socio-demographic data
        • Available from a number of sources
      • Attitudinal or survey data
      • Cost and revenue data
    • Utilizing in-depth data mining techniques determine what your customers look like across all the variables
      • Best if you can avoid selecting the few that you “think” are important
  • 13. Data Mining
    • Is the process of sorting through large amounts of data and picking out relevant information
    • The nontrivial extraction of implicit, previously unknown, and potentially useful information from data
    • A form of business intelligence
      • Historical
  • 14. Predictive Modeling
    • Use of a variety of techniques from statistics and data mining to analyze current and historical data to make predictions about future events
      • Crystal ball based on objectivity!
    • Leverages good data mining to look to the future
      • Who is likely to do what and when
  • 15. Achieving Your Goals
    • Who is likely to be relevant and appropriate to target for what
      • Irrespective of whether they are loyal, highest revenue generating, most profitable
      • Look at customers a number of directions to view those who fit under other segments or labels
      • Allows you to understand your strategies better
    • Understanding what works for whom
  • 16. It Works!
    • Leverage your in-depth data mining to predict who to target for what, when, and via what media to have the highest likelihood of achieving your goals
    • Allow new events within your data to update the “scoring” so that you continue to reach out to your customers when it is relevant
      • Improves results to 20-60%
  • 17. Predictive Modeling vs Business Intelligence
    • Predictive modeling allows you to make decisions for the future with some certainty of what the future will bring
    • BI is historical information from your data
      • History is very important in order to predict
      • Looking in the rear view mirror is not likely to get you where you need to go
    • Generally you use BI to anticipate what is likely to happen in the future
      • Without using modeling to assist, these are educated guesses
  • 18. Successful Implementation
    • Focus on the business needs first
      • How will the results be used
      • What are you willing to do based on the results
      • How will you implement the results throughout your organization
      • Determine what will be a “homerun” from the project
    • Engage your modeling department to determine internal competency to reach your goals
      • Seek external resources as needed
      • Understand the capabilities of the vendor
        • Ask for detailed disclosure of costs and deliverables
  • 19. Collaboration
    • Include IT early in the phase
      • Understand technical definitions of data
      • Access to the data
    • Review the results
    • Create “interventions” to act on the results
    • Align your department to respond accordingly
    • Continue to measure your results and adapt accordingly to meet your goals
  • 20. Improved Implementation
    • Software-as-a-service
      • Quicker implementation
      • No need to buy software or hardware
      • No need for internal expertise
    • Learning system
      • Eliminates the need for updating of models
    • Real-time analysis expands usability in the organization
    • Solution versus tool
      • Integrated within business processes to drive results at point of contact or interaction
      • No need for consultants to use the tool
  • 21. Improved Implementation
    • Predictions at the individual level maximize results
    • Information to multiple levels within an organization keeps everyone focused on the same goals
  • 22. Pitfalls to Avoid
    • One size fits all products or solutions
    • Canned segmentation and profiles
    • Implementations that are not well confined in terms of investment and time
    • Beginning without a benchmark of success defined
      • Go/no go decisions
    • Implementing without an intervention plan of if/then
  • 23. Conclusions
    • Customer Value Management provides you the opportunity to value each customer appropriately in the right circumstances
    • Achieving maximum results requires you to understand the optimum cross-section between quality and quantity
    • Results are astounding in relation to traditional marketing approaches
  • 24. About Adaptive Technologies, Inc. (ATi)
    • ATi empowers business leaders to make better decisions.
    • We provide each client with tailored business intelligence and advanced predictive analytics to drive business success.
    • Our software-as-a-service solution turns your cross-enterprise data into actionable Intelligent Information.
    • Business leaders choose ATi's advanced predictive analytics because we analyze data better, faster and more cost-effectively.
  • 25. ATi empowers you to DECIDE. WITH CONFIDENCE. Adaptive Technologies, Inc. (ATi) 4530 East Shea Boulevard, Suite 130 Phoenix, Arizona 85028 602-923-4200 [email_address]