The Positive Impact of Data Mining:
Personalized Communications & Marketing
In the 5th Annual Survey (2011) data miners shared examples of situations where data mining is having a positive impact on society. A summary of the top five positive impact example topic areas is available. Below is the full text of the positive impact examples they shared in the topic area of Personalized Communications & Marketing:
- As we are working mainly for a marketing audience, we have proven to make marketing more efficient (+10% revenue) and more relevant (+300% more relevant) by applying predictive analytics for targeting and customized offering.
- Netflix recommender and Google Reader's "sort by magic" have completely revolutionized the way I consume information for entertainment and news.
- Automation of keyword pricing for bidding on search engines. Automation of "good" keyword detection.
- Amazon customer experience embodies data mining.
- In a world where people are inundated with marketing, data mining can create an environment that a consumer hears what they want to hear about.
- Discover information in text. Predict who is likely to buy, migrate or churn. Improve campaigns.
- Data mining can help quality of life and assist public health interventions - by producing a model predicting the effect of an intervention. Data mining can help filter important data and information from spam and information overload.
- Fairer pricing and product conditions to customers, reflecting their own behaviours and minimizing the "free riding problem" of risky customers causing costs of good customers to rise.
- Spam filtering, automatic recommendations for culture discovery.
- Data Mining makes better use of resources. It also allows us to identify people in need of services.
- Target less customer but interested customers. Too many spam emails could be reduced by better targeting the customers.
- Lower volume of direct marketing campaign, optimize collections.
- Recommendation engines really help the customers (when they work ...). Better customer targeting (in order to avoid spams ...). Better understanding of customers behavior.
- It can certainly help companies focus their limited marketing resources on the most productive sales channels.
- 1) Recommendation systems for pretty much anything. You can't buy/use etc. something you don't know about. 2) Social networking 3) Fraud, criminal and terrorist detection. 4) Improving on human errors (auto correcting human spelling in searches, suggesting other queries etc.) 5) Anomaly detection.
- "Data Mining could contribute for: - more targeted advertising. Senior person may want to see relevant advertisement to his age and interest. If the response rate is tracked, people could benefit from the most relevant content and use it more, instead of simply disregarding the endless advertisement. - Predictive analytics may help families to wisely plan their budget for years to come (after school care, activities, college, summer camps, vacation, bar mitzvah celebration etc.). For instance, learning the past spendings of families with x kids living in state Y, a future spending plan could be built given parameters such as zip code, number of kids, age of kids (pets are optional), religion etc.
- Enhancement of existing: - preventing Money Laundry at International level. - Predicting most likely locations of nature resources (oil, gas) by mapping an existing distribution of spots. - Prediction earthquakes, based on the past patterns - Healthcare - economy of scale: documenting all treatments, patient data, allergic reactions etc. and unifying it to a large data mart (example KaiserPermanente) may help to treat similar deceases more precisely, match a right prescription medication (based on previously known side effects). Aka ""evidence based medicine"". No doubt, the data has to be secured."
- Marketing campaigns - less spam.
- Customer segmentation
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