Predicting Partner’s Digital Transformation Based on Artificial Intelligence
Abstract
:1. Introduction
- (1)
- How to identify partners who are transforming or are ready to transform across one or many of the transformation dimensions (such as New Buying Centers, Platform-Based Outcomes, and Customer Experience Life cycle);
- (2)
- When a partner is transforming along one or many of the transformation dimensions, how do their economics change;
- (3)
- Can we spot any unethical behavior by looking through the deal transaction data, which could put all of this at risk?
- (1)
- Generalize useful data into a new database from raw data and denoise data;
- (2)
- Propose an efficient machine-learning model to predict partner transformation;
- (3)
- Analyze a transformation partner regarding their economic change;
- (4)
- Introduce a novel algorithm based on the model to identify partners with unethical behavior.
2. Related Work
2.1. Data-Driven Method
2.2. The Six Ts of Transformation Model
2.3. Building Blocks of the Successful Digital Transformation Method
3. Methodology
3.1. Data Preprocess and Feature Engineering
3.2. Machine-Learning Model Solution
3.3. VKR Algorithm Analysis
Algorithm 1: Hybrid VKR (VAE, K-means, random forest) algorithm |
Input: Data matrix , there m is total data number and n is the dimension of each data |
1. Deal with missing values(Sparse coding) |
2. Use normalization data to restructure the input data matrix X |
3. Build and training Model |
3.1 Using VAE model to generate latent mean vector |
3.2 Input latent vector to RF (Random Forest) to training model to identify partner transformation |
3.3 Input latent vector to K-means to training model to identify an unethical issue |
4. Using training VKR model to the prediction result |
5. Using a random forest algorithm to analyze the important indicators |
6. When meeting convergence condition output result |
Output: Output matrix about the clustering result |
4. Experiment
4.1. Dataset
4.2. Experimental Method
5. Results and Discussion
5.1. How Do We Identify Partners Who Are Transforming or Are Ready to Transform across One or Many of the Transformation Dimensions
5.1.1. The Result of Prediction Transformation Partners
5.1.2. Importance of the Transformation Dimensions Analysis
5.1.3. Discussion
5.2. When a Partner Is Transforming along One or Many of the Transformation Dimensions, How Do Their Economics Change?
5.2.1. Relevant Data Collection
5.2.2. Data Analysis
5.2.3. Data Comparison Diagram
5.2.4. Discussion
5.3. Can We Spot Any Unethical Behavior by Looking through Our Deal Transaction Data?
5.3.1. Anomaly Detection
5.3.2. Discussion
6. Conclusions and Prospect
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No. | Dataset Name | Table Name |
---|---|---|
1 | reference_ss | customers_d |
2 | reference_ss | deals_d |
3 | reference_ss | partners_d |
4 | reference_ss | products_d |
5 | reference_ss | pv_fiscal_day_to_year |
6 | reference_ss | pv_sales_hierarchy |
7 | sales_ss | bookings_f |
8 | sales_ss | pipeline_f |
9 | sales_ss | pv_bookings_channel_measure |
10 | sales_ss | pv_cs_deal_so_line_link |
11 | sales_ss | pv_sales_order_line |
12 | sales_ss | pv_sales_order_tv |
13 | sales_ss | pv_sol_end_customer |
14 | services_ss | df_installed_product_f |
Field Name | Type | Description |
---|---|---|
partner_site_party_key | INTEGER | PartnerId which we want to identify |
annual_rcrr_rev_2017 | NUMERIC | recurring revenue in 2017 |
annual_rcrr_rev_2018 | NUMERIC | recurring revenue in 2018 |
nonrecurring_rev_2017 | NUMERIC | nonrecurring revenue in 2017 |
nonrecurring_rev_2018 | NUMERIC | nonrecurring revenue in 2018 |
order_cnt_2017 | INTEGER | the number of orders the partner participated in in 2017 |
order_cnt_2018 | INTEGER | the number of orders the partner participated in in 2018 |
muti_partner_deal_id_amt | INTEGER | number of muti_partner_deal each partner involved in |
rate_of_2017_2018 | FLOAT | the ratio of multiparter deals to all kinds of deals per year |
rate_of_2018_2019 | FLOAT | the ratio of multiparter deals to all kinds of deals per year |
rate_of_2019_2020 | FLOAT | the ratio of multiparter deals to all kinds of deals per year |
rate_of_revenue_type_new | FLOAT | the percentage of new revenue |
rate_of_hardware | FLOAT | the percentage of hardware booking |
partner_site_key | annual_rev_2017 | annual_rev_2018 | order_cnt_2017 | order_cnt_2018 | rate_of_revenue | rate_of_hardware | Predict |
---|---|---|---|---|---|---|---|
10018921 | 0.00E + 00 | 20442 | 24 | 43 | 0.94846009 | 0.18667505 | 1 |
4894595 | 0.00E + 00 | 12003.36 | 181 | 253 | 0.99975032 | 0.45575752 | 1 |
13451993 | 0.00E + 00 | 0.00E + 00 | 21 | 42 | 0.99270483 | 0.2295174 | 1 |
157253070 | 1578.72 | 0.00E + 00 | 24 | 36 | 0.96423562 | 0.19565217 | 1 |
12732302 | 0.00E + 00 | 28056 | 14 | 43 | 0.83266325 | 0.17856648 | 1 |
3480934 | 0.00E + 00 | 0.00E + 00 | 12 | 29 | 0.98648649 | 0.04504505 | 1 |
197212382 | 0.00E + 00 | 0.00E + 00 | 45 | 59 | 0.96057866 | 0.08788427 | 1 |
215969797 | 0.00E + 00 | 0.00E + 00 | 6 | 19 | 0.49645669 | 0.26122047 | 1 |
9823118 | 0.00E + 00 | 0.00E + 00 | 14 | 43 | 1 | 0 | 1 |
5635975 | 0.00E + 00 | 142830 | 0 | 21 | 1 | 0.44736842 | 1 |
210118079 | 18000 | 1500000 | 153 | 296 | 1 | 0.1515625 | 1 |
2963274 | 0.00E + 00 | 0.00E + 00 | 476 | 1283 | 1 | 0 | 1 |
11864141 | 0.00E + 00 | 0.00E + 00 | 10 | 34 | 0.96503497 | 0.04195804 | 1 |
564774 | 0.00E + 00 | 44004 | 80 | 208 | 0.96732889 | 0.06323015 | 1 |
222156482 | 23148.84 | 826367.4 | 384 | 665 | 0.69586588 | 0.19068726 | 1 |
98668929 | 2787 | 0.00E + 00 | 26 | 45 | 0.79325843 | 0.0411236 | 1 |
7897872 | 1671.6 | 0.00E + 00 | 29 | 83 | 0.93974556 | 0.22997344 | 1 |
143938004 | 0.00E + 00 | 1094.16 | 28 | 107 | 0.93018403 | 0.10434453 | 1 |
203076171 | 0.00E + 00 | 0.00E + 00 | 42 | 44 | 0.70868347 | 0.19794585 | 1 |
209689932 | 0.00E + 00 | 0.00E + 00 | 1 | 38 | 0.71361502 | 0.13849765 | 1 |
162572460 | 0.00E + 00 | 0.00E + 00 | 5 | 17 | 0.61871286 | 0.13646139 | 1 |
230369460 | 271542.12 | 1310853.96 | 28 | 76 | 1 | 0.31467964 | 1 |
100342751 | 6573.84 | 303184.08 | 6 | 144 | 1 | 0 | 1 |
12102582 | 6280.32 | 11995.68 | 13 | 28 | 1 | 0.25290698 | 1 |
Booking | Revenue | Gross Margin | Deal Size | Deal Velocity |
---|---|---|---|---|
rebate_bookings | annual_rcrr_rev_trxl_amt | sum_tss_gross_margin | list_price | shipment_confirmed_date |
annualized_bookings | dv_annual_rcrr_rev_usd_amt | sum_product_bookings_gross_margin | cost | booked_date |
actual_bookings | mthly_rcrr_rev_trxl_amt | base_price | ||
nonrecurring_rev_trxl_amt |
partner_site_party_key | pre_discount_credit_sum | rebate_bookings_sum |
---|---|---|
222124838 | 863,944.85 | −24,060 |
167488506 | 23,364 | −1610 |
216478480 | 99,774,44 | −550 |
5362616 | 30,999,96 | 0 |
196968850 | 800 | 0 |
3105456 | 99,000 | 0 |
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He, C.; H. Q. Ding, C. Predicting Partner’s Digital Transformation Based on Artificial Intelligence. Appl. Sci. 2022, 12, 91. https://doi.org/10.3390/app12010091
He C, H. Q. Ding C. Predicting Partner’s Digital Transformation Based on Artificial Intelligence. Applied Sciences. 2022; 12(1):91. https://doi.org/10.3390/app12010091
Chicago/Turabian StyleHe, Chenggang, and Chris H. Q. Ding. 2022. "Predicting Partner’s Digital Transformation Based on Artificial Intelligence" Applied Sciences 12, no. 1: 91. https://doi.org/10.3390/app12010091
APA StyleHe, C., & H. Q. Ding, C. (2022). Predicting Partner’s Digital Transformation Based on Artificial Intelligence. Applied Sciences, 12(1), 91. https://doi.org/10.3390/app12010091