"Being creative with data" 25th November - KXEN presentation
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Alan Furness from KXEN at the IPA's "Being creative with data" workshop and seminar on Wednesday 25th November.
![Agenda ,[object Object],[object Object],[object Object],[object Object],KXEN, Inc Company Confidential](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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"Being creative with data" 25th November - KXEN presentation
- 1. Using Predictive Analytics Presented by: Alan Furness November 25 th 2009
- 4. Your Environment and Goal Change over Time KXEN, Inc Company Confidential Requires More Focused Models Requires More Current Data Requires Frequent Updates Markets Are Different Customers Are Different Products/Channels are Different Your Strategies Change Customer Situations Change The Environment Changes
- 6. Too much data…..? KXEN, Inc Company Confidential Have you got lots of data? Are you getting Value from it? Analytics is about Insight
- 10. Behavioral-Based Insight KXEN, Inc Company Confidential Thousands of Audience Segments Thousands of Potential Advertising Placements Ingest Large Volumes of Self-expressed User Data Deliver Real-time Recommendations and Predictions Model Dynamic Continuous Fast cycle Next Best Offer Next Best Activity Marketing Mix Retain Cross-sell Attribution Acquire Next Best Product
- 15. Example : Ad Attribution for Financial Services Provider (cont.) KXEN, Inc Company Confidential Solution Key Drivers Variable % % of impressions from the site they visit most often 21% number of creatives seen 19% internet domain 13% site page they see most often 11% city 8% number of days with impressions 7% DMA 6% site they see most often 5% number of operating systems 5% number of domains 4% Top Referring Site Freq 1 - Crossed Frequency Fool.com, KxOther, Seeking Alpha 1.32% 4.51% CNNMoney, TheStreet.com 3.44% 2.29% Bloomberg.com, Silicon Alley Media , Vibrant Media Inc, Wall Street Journal Online, Yahoo 39.76% 1.24% InvestingChannel, MSN 36.57% 0.75% AOL.com, Turn, Inc., United 18.92% 0.53%
- 20. Thank You
Notes de l'éditeur
- Over time your business environment and strategic goals will change. Markets change. Competitors add new features and services, the economy expands or shrinks, new technologies and products come to market. Those changes in the market drive the need for changes in your strategy. And at the same time, customers change their behavior in response to changes in the marketplace. All of these changes drive the need for analytics that help you understand and predict customer behavior. However, at the end of the day the Marketing Director is not looking for more models, but more insight at the speed of (his/her) business, in order to make better decisions.
- Let’s take a look at what happens when you employ traditional data mining technologies: The business user starts out with a business question: “As the luxury car dealer you want to know who of your mailing list is likely to earn over a certain income e.g. $50,000” Now you have to get hold of an analyst from your statistical department to help you with your project You explain your problem to the analyst who starts by working on the data that you have for him; s/he will look for outliers, identify missing values, try to reduce the number of attributes that you want to use… The first step is to reduce the number of variables from perhaps 800 (that are available in your data Warehouse) to about 20 – 30 that a traditional modeling tool can handle The next step is to prepare the data for modeling – this is where the expert spends 60% to 90% of the time: Data Mining tools can only handle numbers, so all text information has to be converted to numeric values, Outliers have to be discarded, missing values have to be replaced and much more Then the analyst is ready to build a great model and it actually explains your data very well But when you try to run this model on new data, the results are bad – so the analyst goes back and tweaks the model. An then s/he tries it again on new data and that way, after several iterations, a compromise is found between fit and robustness Now the analyst has to interpret the resulting several pounds of paper that running the model has produced so that you understand what happened 3 weeks for this process is considered to be aggressive - the average cost per model is somewhere between $20,000 – $100,000, which accounts for the time of the analyst and the systems and software tools KXEN proposes to shrink this process to hours or minutes at a cost of less than $500 per model, which can quickly converge to 0 as customers build high numbers of models
- Analytics is not rocket science anymore! And we do not have to be rocket scientists! Tools are available that enable business analysts close to the business people to begin delivering insight into the meaning of the organisation’s data Finally no insight has any value unless it can be actioned profitably. To optimise that value there is a another stakeholder in the mix – the agencies, the creatives who can take that insight from the analysts and quickly turn it into an appropriate and compelling action. The circle needs to be closed.