Example 21-1 Predict Best Candidates for an Affinity Card

SELECT cust_id
  FROM mining_data_apply_v
  WHERE
      PREDICTION(dt_sh_clas_sample 
                   USING cust_marital_status, education, cust_income_level ) = 1
  AND country_name IN 'Brazil';

  CUST_ID
----------
    100404
    100607
    101113

The same query, but with a bias to favor false positives over false negatives, is shown here.

SELECT cust_id
  FROM mining_data_apply_v
  WHERE
      PREDICTION(dt_sh_clas_sample COST MODEL
                   USING cust_marital_status, education, cust_income_level ) = 1
  AND country_name IN 'Brazil';

  CUST_ID
----------
    100139
    100163
    100275
    100404
    100607
    101113
    101170
    101463

The COST MODEL keywords cause the cost matrix associated with the model to be used in making the prediction. The cost matrix, stored in a table called dt_sh_sample_costs, specifies that a false negative is eight times more costly than a false positive. Overlooking a likely candidate for the promotion is far more costly than including an unlikely candidate.

SELECT * FROM dt_sh_sample_cost;
 
ACTUAL_TARGET_VALUE PREDICTED_TARGET_VALUE       COST
------------------- ---------------------- ----------
                  0                      0          0
                  0                      1          1
                  1                      0          8
                  1                      1          0

Example 21-2 Find Demographic Information About Preferred Customers

This query returns the gender, age, and length of residence of typical affinity card holders. The anomaly detection model, SVMO_SH_Clas_sample, returns 1 for typical cases and 0 for anomalies. The demographics are predicted for typical customers only; outliers are not included in the sample.

SELECT cust_gender, round(avg(age)) age,
       round(avg(yrs_residence)) yrs_residence,
       count(*) cnt
FROM mining_data_one_class_v
WHERE PREDICTION(SVMO_SH_Clas_sample using *) = 1
GROUP BY cust_gender
ORDER BY cust_gender;
 
CUST_GENDER         AGE YRS_RESIDENCE        CNT
------------ ---------- ------------- ----------
F                    40             4         36
M                    45             5        304

Example 21-3 Dynamically Identify Customers Who Resemble Preferred Customers

This query identifies customers who do not currently have an affinity card, but who share many of the characteristics of affinity card holders. The PREDICTION and PREDICTION_PROBABILITY functions use an OVER clause instead of a predefined model to classify the customers. The predictions and probabilities are computed dynamically.

SELECT cust_id, pred_prob 
 FROM
  (SELECT cust_id, affinity_card,
    PREDICTION(FOR TO_CHAR(affinity_card) USING *) OVER () pred_card,
    PREDICTION_PROBABILITY(FOR TO_CHAR(affinity_card),1 USING *) OVER () pred_prob
   FROM mining_data_build_v)
 WHERE affinity_card = 0
  AND pred_card = 1
 ORDER BY pred_prob DESC;

  CUST_ID PRED_PROB
---------- ---------
    102434       .96
    102365       .96
    102330       .96
    101733       .95
    102615       .94
    102686       .94
    102749       .93
.
.
.
.
    102580       .52
    102269       .52
    102533       .51
    101604       .51
    101656       .51
 
226 rows selected.

Example 21-4 Predict the Likelihood that a New Customer Becomes a Preferred Customer

This query computes the probability of a first-time customer becoming a preferred customer (an affinity card holder). This query can be executed in real time at the point of sale.

The new customer is a 44-year-old American executive who has a bachelors degree and earns more than $300,000/year. He is married, lives in a household of 3, and has lived in the same residence for the past 6 years. The probability of this customer becoming a typical affinity card holder is only 5.8%.

SELECT PREDICTION_PROBABILITY(SVMO_SH_Clas_sample, 1 USING
                             44 AS age,
                             6 AS yrs_residence,
                             'Bach.' AS education,
                             'Married' AS cust_marital_status,
                             'Exec.' AS occupation,
                             'United States of America' AS country_name,
                             'M' AS cust_gender,
                             'L: 300,000 and above' AS cust_income_level,
                             '3' AS houshold_size
                             ) prob_typical
FROM DUAL;  
 
PROB_TYPICAL
------------
  5.8

Example 21-5 Use Predictive Analytics to Find Top Predictors

The DBMS_PREDICTIVE_ANALYTICS PL/SQL package contains routines that perform simple data mining operations without a predefined model. In this example, the EXPLAIN routine computes the top predictors for affinity card ownership. The results show that household size, marital status, and age are the top three predictors.

BEGIN
    DBMS_PREDICTIVE_ANALYTICS.EXPLAIN(
        data_table_name      => 'mining_data_test_v',
        explain_column_name  => 'affinity_card',
        result_table_name    => 'cust_explain_result');
END;
/

SELECT * FROM cust_explain_result
  WHERE rank < 4;
 
ATTRIBUTE_NAME           ATTRIBUTE_SUBNAME    EXPLANATORY_VALUE       RANK
------------------------ -------------------- ----------------- ----------
HOUSEHOLD_SIZE                                       .209628541          1
CUST_MARITAL_STATUS                                  .199794636          2
AGE                                                  .111683067          3

Example 21-6 Compute Customer Segments

This query computes natural groupings of customers and returns the number of customers in each group.

SELECT CLUSTER_ID(em_sh_clus_sample USING *) AS clus, COUNT(*) AS cnt
  FROM mining_data_apply_v
GROUP BY CLUSTER_ID(em_sh_clus_sample USING *)
ORDER BY cnt DESC;
 
      CLUS        CNT
---------- ----------
         9        311
         3        294
         7        215
        12        201
        17        123
        16        114
        14         86
        19         64
        15         56
        18         36

Example 21-7 Find the Customers Who Are Most Likely To Be in the Largest Segment

The query in Example 21-6 shows that segment 9 has the most members. The following query lists the five customers who are most likely to be in segment 9.

SELECT cust_id
FROM (SELECT cust_id, RANK() over (ORDER BY prob DESC, cust_id) rnk_clus2
  FROM (SELECT cust_id,
          ROUND(CLUSTER_PROBABILITY(em_sh_clus_sample, 9 USING *),3) prob
          FROM mining_data_apply_v))
WHERE rnk_clus2 <= 5
ORDER BY rnk_clus2;
 
   CUST_ID
----------
    100002
    100012
    100016
    100019
    100021

Example 21-8 Find Key Characteristics of the Most Representative Customer in the Largest Cluster

The query in Example 21-7 lists customer 100002 first in the list of likely customers for segment 9. The following query returns the five characteristics that are most significant in determining the assignment of customer 100002 to segments with probability > 20% (only segment 9 for this customer).

SELECT S.cluster_id, probability prob,
       CLUSTER_DETAILS(em_sh_clus_sample, S.cluster_id, 5 using T.*) det
 FROM
  (SELECT v.*, CLUSTER_SET(em_sh_clus_sample, NULL, 0.2 USING *) pset
    FROM mining_data_apply_v v
    WHERE cust_id = 100002) T,
 TABLE(T.pset) S
 ORDER BY 2 desc;
 
CLUSTER_ID    PROB DET
---------- ------- --------------------------------------------------------------------------------
         9  1.0000 <Details algorithm="Expectation Maximization" cluster="9">
                   <Attribute name="YRS_RESIDENCE" actualValue="4" weight="1" rank="1"/>
                   <Attribute name="EDUCATION" actualValue="Bach." weight="0" rank="2"/>
                   <Attribute name="AFFINITY_CARD" actualValue="0" weight="0" rank="3"/>
                   <Attribute name="BOOKKEEPING_APPLICATION" actualValue="1" weight="0" rank="4"/>
                   <Attribute name="Y_BOX_GAMES" actualValue="0" weight="0" rank="5"/>
                   </Details>