A sales lead scoring design method and medium suitable for user value analysis

Abstract

The present invention discloses a sales lead scoring design method and medium suitable for user value analysis, including: S1): selecting sales leads that should participate in scoring from a database; S2): calculating the basic portrait score of the sales lead based on multiple dimensions; S3): calculating the sales lead activity score; S4): calculating the sales lead effective follow-up score; S5): calculating the preliminary score of the sales lead; S6): calculating the sales lead extra-dimensional weight 1; S7): determining whether the sales lead is the customer's first sales lead and calculating the sales lead extra-dimensional weight 2; S8): calculating the final sales lead score. The present invention processes and analyzes the collected sales leads in multiple dimensions, and the calculated evaluation grade helps sales personnel more accurately identify the needs and preferences of potential customers, optimize resource allocation and sales strategies, better serve users, and ultimately achieve business growth and sustainable development.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular relates to a sales lead scoring design method and medium suitable for user value analysis. Background Art

[0002] With the rapid development of educational informatization, many people are constantly striving to improve themselves. This has given rise to platforms that provide students with personalized learning resources. For example, the postgraduate entrance exam has seen a stabilization in applicants after experiencing explosive growth in recent years. Furthermore, users' preparation paths are gradually shifting, with more and more users shifting from preparing in the same year to even earlier years. Consequently, the number of sales leads continues to increase.

[0003] However, in practice, the sheer volume of sales leads and the inherent complexity of these leads can hinder sales teams from blindly following up on leads or selectively following up on leads within their personal database, making it difficult to quickly identify high-quality leads. Passively following up on leads without knowing their quality can reduce sales follow-up efficiency and severely impact service performance. Therefore, developing a sales lead scoring method is crucial for improving lead conversion efficiency, enhancing the effectiveness of sales decision-making, and promoting business growth. Summary of the Invention

[0004] Purpose of the invention: In order to overcome the above shortcomings, the purpose of the present invention is to provide a sales lead scoring design method suitable for user value analysis. By subjecting the collected sales leads to multi-dimensional data processing and analysis, the calculated evaluation grades will help sales personnel to more accurately identify the needs and preferences of potential customers, optimize resource allocation and sales strategies, better serve users, and ultimately achieve business growth and sustainable development.

[0005] Technical solution: In order to achieve the above purpose, the present invention provides a sales lead scoring design method suitable for user value analysis, including the following steps:

[0006] S1): Circle sales leads that should be included in the scoring from the database. The sales leads that should be included in the scoring must meet the following requirements: the sales leads are not closed, the sales leads are not invalid sales leads, and the sales leads are not in the transaction pool;

[0007] S2): Calculate the basic sales lead profile score based on multiple dimensions; the multiple dimensions are the user's postgraduate entrance examination year, sales stage, and contact method;

[0008] S3): Calculate the sales lead activity score. Calculate the weight of the sales lead record generation method using the TF-IDF algorithm, calculate the time interval between the sales lead record and the current time interval, and use the Gaussian decay function to calculate the time weight. The sum of the product of the sales lead record generation method weight and the time weight is used as the sales lead activity score.

[0009] S4): Calculate the effective follow-up score for sales leads. Select three indicators based on the sales team's effective follow-up of sales leads. Standardize the indicator values ​​by the sales lead's postgraduate entrance examination year. Use the sigmoid function to calculate the score for each indicator. Then, use the entropy weight method to calculate the indicator data weight. Sales personnel determine the indicator business weight based on business rules. The sum of the product of the sales lead effective follow-up indicator score, the indicator data weight, and the indicator business weight is used as the sales lead effective follow-up score.

[0010] S5): Calculate the preliminary score of the sales lead. The sales personnel will determine the weight of each dimension based on the importance of each dimension and the reliability of the information source. The sum of the product of the sales lead score in each dimension and the dimension weight is used as the preliminary score of the sales lead;

[0011] S6): Calculate the weight 1 outside the sales lead dimension, based on the fact that the shorter the creation time interval of the sales lead, the less information the sales lead itself carries. In addition to the dimension score, calculate the interval between the sales lead creation time and the present time, and use the Gaussian decay function to calculate the weight 1 outside the sales lead dimension. The weight value remains 1 within the interval of offset2 days, and begins to decay after offset2 days. After scale2 days, the weight decays to decay2.

[0012] S7): Determine whether the sales lead is the client's first sales lead. Based on the particularity of the postgraduate entrance examination industry, we consider that the higher the average order value of a sales lead, the lower the likelihood of a repeat sales lead. We calculate a weight of 2 for the sales lead dimension. The weight of the first sales lead remains at 1, while the weight of a non-first sales lead is reduced to 0.5.

[0013] S8) Calculate the final sales lead score, that is, take the product of the preliminary score of the sales lead calculated in S5), the out-of-dimensional weight 1 in S6), and the out-of-dimensional weight 2 in S7) as the final sales lead score.

[0014] The sales lead scoring design method and application for user value analysis described in the present invention, wherein the specific process of calculating the sales lead basic profile score in S2) based on the predicted application year, sales stage, and reachability of the circled sales lead is as follows:

[0015]

[0016] Among them, i is the i-th clue, j is the j-th dimension, d j Indicates the weight of the j-th dimension, d_score ij is the score of the i-th sales lead under the j-th dimension;

[0017] Among them, j|∈{1, 2, 3} corresponds to the three dimensions of the customer's postgraduate entrance examination year, the lead sales stage and the customer's reachable method; the sales lead basic portrait score is composed of the three dimensions of the user's postgraduate entrance examination year, the lead sales stage and the customer's reachable method.

[0018] The sales lead scoring design method for user value analysis described in the present invention calculates the scores for each dimension of the user's postgraduate entrance examination year, lead sales stage, and customer reachability as follows:

[0019] When the dimension is the year the user took the postgraduate entrance examination, the score calculation is as follows:

[0020] The user's postgraduate entrance examination year is a label for the user's postgraduate entrance examination year. Since a customer's course purchase intention is related to the purchase time and the customer's postgraduate entrance examination year, customers in different time periods and different preparation periods have different course purchase needs. Therefore, the user's postgraduate entrance examination year score is split into different months for calculation;

[0021] If the sales lead does not have a postgraduate entrance examination year label, it indicates that the user has not left any intention information; if the sales lead has a postgraduate entrance examination year label that has passed, it indicates that the customer currently has no intention to purchase courses again;

[0022] If a sales lead has the tag "this year's postgraduate entrance examination," the customer is more likely to purchase high-priced courses from January to June of the year of the exam. During the summer vacation from July to August, there is still a tendency to purchase courses for the second half of the exam preparation. However, from September to December, as the exam approaches, the tendency to purchase this year's postgraduate entrance examination courses decreases and tends to zero.

[0023] If the sales lead has the label "Next Year's Postgraduate Entrance Examination" or "Future Postgraduate Entrance Examination (later than next year), the customer's preparation path is relatively long, and the user's purchase intention is gradually increasing. However, the closer the postgraduate entrance examination year is to the current year, the greater the purchase intention is.

[0024] Therefore, for sales leads without a postgraduate entrance examination year label or whose postgraduate entrance examination year label has passed, the postgraduate entrance examination year is calculated as follows: Postgraduate entrance examination year score i =0, where i is the i-th clue;

[0025] If a sales lead is extracted with the tag "this year's postgraduate entrance examination year", the score for that year is calculated as follows:

[0026]

[0027] Where i is the i-th clue, t is the month corresponding to the statistical date, and is an integer in the range [1, 12];

[0028] λ1 is the slope of the curve of this year's postgraduate entrance examination label when 7≤t≤8, that is, the decay rate of the curve in July and August;

[0029] λ2 is the additional attenuation coefficient of the curve of this year's postgraduate entrance examination label when 9≤t≤12, which is used to accelerate the attenuation speed of the curve from September to December;

[0030] If a sales lead is extracted with the tag "Next Year's Postgraduate Entrance Examination Year", the score for that year is calculated as follows:

[0031]

[0032] If a sales lead is extracted with the tag "future postgraduate entrance examination year" (later than next year), the score for the postgraduate entrance examination year is calculated as follows:

[0033]

[0034] α1 is the slope of the curve for next year's postgraduate entrance examination label, and α2 is the slope of the curve for future postgraduate entrance examination labels (later than next year), that is, the growth rate and satisfies α2<α1. Larger α1 and α2 values ​​will make the postgraduate entrance examination year score grow faster near the center point, while smaller α1 and α2 values ​​will make the postgraduate entrance examination year score grow more slowly;

[0035] β1 is the center point of the curve for next year's postgraduate entrance examination label, and β2 is the center point of the curve for future postgraduate entrance examination labels (later than next year), which is the point where the score increases fastest in the postgraduate entrance examination year;

[0036] When the dimension is lead sales stage, the calculation method is as follows:

[0037] The lead sales stage is recorded in the CRM system, and the sales staff will mark the sales lead after following up;

[0038] Sales leads can be in any of the above sales stages before winning a deal, so the sales stage score is based on the historical winning sales lead data. The proportion of sales leads in a certain sales stage to all winning sales leads is the sales stage score, that is,

[0039]

[0040] Among them, i is the i-th clue;

[0041] When the dimension is customer reachability, the score is calculated as follows:

[0042] The customer's contactability is recorded in the CRM system, which is composed of whether the customer has a mobile phone number or system account, whether the customer has a valid friend relationship, and whether the customer has a valid group chat relationship.

[0043] If the customer has a mobile phone number or system account, sales staff can reach the customer through outbound calls, SMS, and APP private messages;

[0044] If the customer has a valid friend relationship, the salesperson can communicate with the customer directly through WeChat for Business or other software;

[0045] If the customer has an effective group chat relationship, the salesperson can reach the customer by sending group chat messages.

[0046] Customers can be reached in multiple ways at the same time. The more diverse the customer's reachable ways are, the more diverse the salesperson's reach methods are, and the higher the possibility of reaching the customer. Therefore, the reachable way score is based on historical winning sales lead data, that is,

[0047]

[0048] Among them, i is the i-th clue, and j is the j-th accessible method.

[0049] The sales lead score design method and application for user value analysis described in the present invention, the specific algorithm for calculating the weight value of the sales lead record generation method based on the TF-IDF algorithm in S3) is as follows:

[0050] The weight of the sales lead record generation method is calculated based on the TF-IDF algorithm. TF-IDF is TF*IDF, which is the term frequency * inverse text frequency index. In this context, the term frequency is analogous to the proportion of sales lead records corresponding to a sales lead generation method to all sales lead records for a certain winning sales lead.

[0051] The inverse text frequency index calculation formula is:

[0052] This can be compared to the ratio of all won leads to leads with a certain lead generation method. To avoid this value being zero and increase discrimination, the denominator is increased by 1 and logarithmic transformation is performed.

[0053] Therefore,

[0054] The time weight calculation process of the sales lead activity score is as follows:

[0055] The sales lead activity score is a comprehensive score calculated based on the sales lead records reported by the sales lead, the weight of the sales lead record generation method calculated through historical win data, and the time factor;

[0056] Based on the idea that the longer the gap between a sales lead record and today's reporting time, the less important it is, time weighting can emphasize the importance of the latest sales lead record while not neglecting the contribution of sales lead records reported earlier. The time weight is based on the Gaussian decay function, and the formula for setting the time weight of the gap between the reporting time of a sales lead record and today's time is as follows:

[0057]

[0058] Among them, i is the i-th clue, x i Indicates the number of days between the reporting time of the i-th sales lead record and the present time;

[0059] Offset1 is the offset of the sales lead record time weight. The weight of sales lead records reported within [0, offset1] days is set to 1 and remains unchanged. The weight starts to decay after offset1 day.

[0060] scale1 is the decay rate of the sales lead record time weight, that is, the speed at which the sales lead record time weight changes;

[0061] decay1 is the sales lead record time weight that decays from 0 days to the sales lead record time weight value corresponding to scale1;

[0062] For each sales lead record, this function can be used to obtain the time weight value of the interval between the sales lead record reporting time and the present time;

[0063] Reference parameter values: offset1=14, scale1=90, decay1=0.5;

[0064] Thus,

[0065] The sales lead activity score is the sum of the scores of all sales lead records reported by the sales lead.

[0066] The sales lead scoring design method and application for user value analysis of the present invention, the specific calculation process of the sales lead effective follow-up score in S4) is as follows:

[0067]

[0068] Where i represents the i-th sales lead, j represents the j-th indicator, and biz_weigh jIndicates the business weight of the j-th indicator, data-weigh j represents the data weight of the j-th indicator, x ij It represents the effective follow-up score of the i-th sales lead under the j-th indicator.

[0069] The sales lead scoring design method and application suitable for user value analysis described in the present invention is characterized in that: the effective follow-up index score x of the i-th sales lead under the j-th index is ij The calculation method is as follows:

[0070] In the same cycle, sales have different follow-up strategies for customers with different postgraduate entrance examination years. Therefore, when calculating the follow-up index score, the index values ​​are standardized based on the different postgraduate entrance examination years of the customers, that is,

[0071]

[0072] Among them, mean is the mean of the index values ​​grouped by postgraduate entrance examination year, and std is the standard deviation of the index values ​​distributed by postgraduate entrance examination year, so that the index values ​​of different years all satisfy the normal distribution with a mean of 0 and a standard deviation of 1.

[0073] The sales lead scoring design method and application suitable for user value analysis described in the present invention, wherein the three indicators of the sales lead effective follow-up score are the number of effective follow-ups, the effective follow-up rate and the interval from the last effective follow-up.

[0074] Among them, the number of effective follow-up times and the effective follow-up rate are positive indicators, and the larger the indicator value, the higher the score; the interval since the last effective follow-up is a negative indicator, and the smaller the indicator value, the higher the score:

[0075] Based on the indicator standardization results, the sigmoid function is used to calculate the scores of each sales lead indicator. The calculation formula is as follows:

[0076] Positive indicators are:

[0077] Negative indicators are:

[0078] Among them, S ij is the normalized result of the indicator value of the i-th sales lead under the j-th indicator.

[0079] The sales lead score design method and application for user value analysis described in the present invention, the calculation process of the final sales lead score in S8) is as follows:

[0080]

[0081] Among them, i represents the i-th sales lead, j represents the j-th dimension, weigh1 i The weight of the ith sales lead outside the dimension is 1, that is, the weight of the interval between the creation of the sales lead and the present;

[0082] weigh2 i Indicates the weight of the i-th sales lead outside the dimension 2, that is, the weight of whether it is the customer's first sales lead, D_weigh j Indicates the weight of the j-th dimension, D_score ij Represents the score of the i-th sales lead under the j-th dimension.

[0083] The weigh1 i is calculated as follows:

[0084] The formula for setting the time weight for the sales lead creation interval is as follows:

[0085]

[0086] Among them, i represents the i-th sales lead, x 2_i Indicates the number of days since the creation of the i-th sales lead.

[0087] weigh1 i The out-of-dimension weight of the i-th sales lead is 1, that is, the time weight of the sales lead creation interval;

[0088] offset2 is the offset of weight 1 outside the sales lead dimension. The weight of sales leads created within [0, offset2] days is set to 1 and remains unchanged. The weight starts to decay after offset2 days.

[0089] scale2 is the decay rate of the weight 1 outside the sales lead dimension, that is, the speed at which the weight 1 outside the sales lead dimension changes;

[0090] decay2 is the value of the weight 1 outside the sales lead dimension that decays from 0 days to the weight 1 outside the sales lead dimension corresponding to scale2;

[0091] For each sales lead, this function can be used to obtain the value of weight 1 outside the sales lead dimension.

[0092] The sales lead scoring design method and application suitable for user value analysis described in the present invention, the weigh2 i is 1 or 0.5.

[0093] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the sales lead score design method suitable for user value analysis as described above is implemented.

[0094] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0095] 1. The present invention provides a sales lead scoring design method suitable for user value analysis, which processes and analyzes the collected sales leads in multiple dimensions to obtain a basic portrait score of the sales leads, and then calculates the preliminary score of the sales leads based on the proportion of sales lead record generation methods, the sales lead activity score, and the sales lead effective follow-up score. The final sales lead score is obtained by the preliminary score of the sales lead, the out-of-dimensional weight 1, and the out-of-dimensional weight 2, so that evaluation and grading can be performed according to the final sales lead score, which helps sales personnel to more accurately identify the needs and preferences of potential customers, optimize resource allocation and sales strategies, better serve users, and ultimately achieve business growth and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 This is a schematic diagram of the structure of the sales lead scoring design method and application suitable for user value analysis according to the present invention;

[0097] Figure 2 This is a flowchart of the sales lead activity score calculation machine in the present invention;

[0098] Figure 3 is the weight of the generation method in the present invention;

[0099] Figure 4 This is a flow chart for calculating the effective follow-up score of sales leads in the present invention;

[0100] Figure 5 This is the definition of the sales stage in the basic portrait score in the present invention. DETAILED DESCRIPTION

[0101] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments.

[0102] Example

[0103] like Figure 1 A sales lead scoring design method suitable for user value analysis is shown, including the following steps:

[0104] S1): Circle the sales leads that should be included in the scoring from the database. The sales leads that should be included in the scoring must meet the following conditions: the sales leads are not closed, the sales leads are not invalid sales leads, and the sales leads are not in the transaction pool;

[0105] S2): Calculate the basic sales lead profile score based on multiple dimensions; the multiple dimensions are the user's postgraduate entrance examination year, sales stage, and contact method;

[0106] S3): Calculate the sales lead activity score. Calculate the weight of the sales lead record generation method using the TF-IDF algorithm, calculate the time interval between the sales lead record and the current time interval, and use the Gaussian decay function to calculate the time weight. The sum of the product of the sales lead record generation method weight and the time weight is used as the sales lead activity score.

[0107] S4): Calculate the effective follow-up score for sales leads. Select three indicators based on the sales team's effective follow-up of sales leads. Standardize the indicator values ​​according to the predicted application year of the sales lead. Use the sigmoid function to calculate the score for each indicator. Use the entropy weight method to calculate the data weight of each indicator. Business personnel determine the business weight of each indicator based on business rules. The sum of the product of the effective follow-up indicator score, the indicator's data weight, and the indicator's business weight is used as the effective follow-up score for sales leads.

[0108] S5): Calculate the preliminary score of the sales lead. The sales personnel will determine the weight of each dimension based on the importance of each dimension and the reliability of the information source. The sum of the product of the sales lead score in each dimension and the dimension weight is used as the preliminary score of the sales lead;

[0109] S6): Calculate the weight 1 outside the sales lead dimension. The shorter the creation time interval of the sales lead, the less information the sales lead itself carries. Based on this, in addition to the scores of each dimension, calculate the interval between the creation time of the sales lead and the present time. Use the Gaussian decay function to calculate the weight 1 outside the sales lead dimension. The weight value remains 1 within the interval of offset2 days. After offset2 days, the weight begins to decay. After scale2 days, the weight decays to decay2.

[0110] S7): Determine whether the sales lead is the client's first sales lead. Based on the particularity of the postgraduate entrance examination industry, we consider that the higher the average order value of a sales lead, the lower the likelihood of a repeat sales lead. We calculate a weight of 2 for the sales lead dimension. The weight of the first sales lead remains at 1, while the weight of a non-first sales lead is reduced to 0.5.

[0111] S8): Calculate the final sales lead score, that is, take the product of the preliminary score of the sales lead calculated in S5), the out-of-dimensional weight 1 in S6), and the out-of-dimensional weight 2 in S7) as the final sales lead score.

[0112] It should be noted that the steps S2) to S4) are not in any particular order. In S3), there may be multiple sales lead records for one sales lead.

[0113] Business personnel determine the weight of each dimension based on the importance of each dimension and the reliability of the information source. The weight assignment rules for each dimension are as follows:

[0114] Since active sales leads are user-generated behavioral records and are not affected by sales staff, they are highly important.

[0115] Sales lead follow-up involves sales personnel reaching out to users through outbound calls, text messages, and WeChat sales. The effectiveness of follow-up is influenced by many factors, including the salesperson's professional capabilities, the user's determination to take the postgraduate entrance exam, and the refinement of follow-up effectiveness rules. Therefore, it is highly important.

[0116] The basic sales lead profile is the basic information of the sales lead. It has low correlation with whether the sales lead is active and whether the sales staff is involved in follow-up, and is of low importance.

[0117] The importance of the intentional dimensions is: sales lead activity > sales lead effective follow-up > sales lead basic portrait, and the initial weights are: sales lead activity score weight 50%, sales lead effective follow-up score weight 30%, sales lead basic portrait score weight 20%.

[0118] The sales lead scoring design method and application suitable for user value analysis described in this embodiment are as follows: Figure 5 As shown, the specific process of calculating the sales lead basic profile score in S2) based on the predicted application year, sales stage, and reachable method of the circled sales lead is as follows:

[0119]

[0120] Among them, i is the i-th clue, j is the j-th dimension, d j Indicates the weight of the j-th dimension, d-score ij is the score of the i-th sales lead under the j-th dimension;

[0121] Among them, j|∈{1, 2, 3} corresponds to the three dimensions of the customer's postgraduate entrance examination year, the lead sales stage and the customer's reachable method; the sales lead basic portrait score is composed of the three dimensions of the user's postgraduate entrance examination year, the lead sales stage and the customer's reachable method.

[0122] In the sales lead scoring design method for user value analysis described in this embodiment, the scores for each dimension of the user's postgraduate entrance examination year, lead sales stage, and customer reachability are calculated as follows:

[0123] When the dimension is the year the user took the postgraduate entrance examination, the score calculation is as follows:

[0124] The user's postgraduate entrance examination year is a label for the user's postgraduate entrance examination year. Since a customer's course purchase intention is related to the purchase time and the customer's postgraduate entrance examination year, customers in different time periods and different preparation periods have different course purchase needs. Therefore, the user's postgraduate entrance examination year score is split into different months for calculation;

[0125] If the sales lead does not have a postgraduate entrance examination year label, it indicates that the user has not left any intention information; if the sales lead has a postgraduate entrance examination year label that has passed, it indicates that the customer currently has no intention to purchase courses again;

[0126] If a sales lead has the tag "this year's postgraduate entrance examination," the customer is more likely to purchase high-priced courses from January to June of the year of the exam. During the summer vacation from July to August, there is still a tendency to purchase courses for the second half of the exam preparation. However, from September to December, as the exam approaches, the tendency to purchase this year's postgraduate entrance examination courses decreases and tends to zero.

[0127] If the sales lead has the label "Next Year's Postgraduate Entrance Examination" or "Future Postgraduate Entrance Examination (later than next year), the customer's preparation path is relatively long, and the user's purchase intention is gradually increasing. However, the closer the postgraduate entrance examination year is to the current year, the greater the purchase intention is.

[0128] Therefore, for sales leads without a postgraduate entrance examination year label or whose postgraduate entrance examination year label has passed, the postgraduate entrance examination year is calculated as follows: Postgraduate entrance examination year score i =0, where i is the i-th clue;

[0129] If a sales lead is extracted with the tag "this year's postgraduate entrance examination year", the score for that year is calculated as follows:

[0130]

[0131] Where i is the i-th clue, t is the month corresponding to the statistical date, and is an integer in the range [1,12];

[0132] λ1 is the slope of the curve of this year's postgraduate entrance examination label when 7≤t≤8, that is, the decay rate of the curve in July and August;

[0133] λ2 is the additional attenuation coefficient of the curve of this year's postgraduate entrance examination label when 9≤t≤12, which is used to accelerate the attenuation speed of the curve from September to December;

[0134] If a sales lead is extracted with the tag "Next Year's Postgraduate Entrance Examination Year", the score for that year is calculated as follows:

[0135]

[0136] If a sales lead is extracted with the tag "future postgraduate entrance examination year" (later than next year), the score for the postgraduate entrance examination year is calculated as follows:

[0137]

[0138] α1 is the slope of the curve for next year's postgraduate entrance examination label, and α2 is the slope of the curve for future postgraduate entrance examination labels (later than next year), that is, the growth rate and satisfies α2<α1. Larger α1 and α2 values ​​will make the postgraduate entrance examination year score grow faster near the center point, while smaller α1 and α2 values ​​will make the postgraduate entrance examination year score grow more slowly;

[0139] β1 is the center point of the curve for next year's postgraduate entrance examination label, and β2 is the center point of the curve for future postgraduate entrance examination labels (later than next year), that is, the point where the score increases fastest in the postgraduate entrance examination year;

[0140] When the dimension is lead sales stage, the calculation method is as follows:

[0141] The lead sales stage is recorded in the CRM system, and the sales staff will mark the sales lead after following up;

[0142] Sales leads can be in any of the above sales stages before winning a deal, so the sales stage score is based on the historical winning sales lead data. The proportion of sales leads in a certain sales stage to all winning sales leads is the sales stage score, that is,

[0143]

[0144] Among them, i is the i-th clue;

[0145] When the dimension is customer reachability, the score is calculated as follows:

[0146] The customer's contactability is recorded in the CRM system, which is composed of whether the customer has a mobile phone number or system account, whether the customer has a valid friend relationship, and whether the customer has a valid group chat relationship.

[0147] If the customer has a mobile phone number or system account, sales staff can reach the customer through outbound calls, SMS, and APP private messages;

[0148] If the customer has a valid friend relationship, the salesperson can communicate with the customer directly through WeChat for Business or other software;

[0149] If the customer has an effective group chat relationship, the salesperson can reach the customer by sending group chat messages.

[0150] Customers can be reached in multiple ways at the same time. The more diverse the customer's reachable ways are, the more diverse the salesperson's reach methods are, and the higher the possibility of reaching the customer. Therefore, the reachable way score is based on historical winning sales lead data, that is,

[0151]

[0152] Among them, i is the i-th clue, and j is the j-th accessible method.

[0153] In the sales lead score design method and application for user value analysis described in this embodiment, the specific algorithm for calculating the weight value of the sales lead record generation method based on the TF-IDF algorithm in S3) is as follows:

[0154] The weights of the sales lead record generation methods shown are calculated based on the TF-IDF algorithm. TF-IDF is TF*IDF, which is the term frequency * inverse text frequency index. In this context, term frequency is analogous to the proportion of sales lead records corresponding to a sales lead generation method to all sales lead records for a certain winning sales lead.

[0155] The inverse text frequency index calculation formula is:

[0156] This can be compared to the ratio of all won leads to leads with a certain lead generation method. To avoid this value being zero and increase discrimination, the denominator is increased by 1 and logarithmic transformation is performed.

[0157] Therefore,

[0158] like Figure 2 Based on the sales lead records reported by the winning sales leads in the past 6 months, the TF-IDF weight value of each sales lead record generation method is calculated.

[0159] like Figure 3 As shown, users generate behavioral data on the terminal, and the corresponding sales leads report the sales lead records of the corresponding generation method to obtain the sales lead records of the past 6 months. The number of sales lead records is calculated based on the sales lead record generation method, the number of sales lead records is calculated based on the sales lead, and the number of sales lead records for each generation method is calculated based on the sales lead;

[0160] Then calculate the importance of each sales lead generation method:

[0161] Importance of Sales Lead A Generation Method A = (Number of Sales Lead A Generation Method A Records / Number of Sales Lead A Records) * lg (Number of Winning Sales Leads / (Number of Winning Sales Leads with Sales Lead Generation Method A Records + 1))

[0162] The weight of the production method = the mean of the importance of the production method.

[0163] The sales lead activity score is a comprehensive score calculated based on the sales lead records reported by the sales lead, the weight of the sales lead record generation method calculated through historical win data, and the time factor;

[0164] The time weight calculation process of the sales lead activity score is as follows:

[0165] The sales lead activity score is a comprehensive score calculated based on the sales lead records reported by the sales lead, the weight of the sales lead record generation method calculated through historical win data, and the time factor;

[0166] Based on the idea that the longer the gap between a sales lead record and today's reporting time, the less important it is, time weighting can emphasize the importance of the latest sales lead record while not neglecting the contribution of sales lead records reported earlier. The time weight is based on the Gaussian decay function, and the formula for setting the time weight of the gap between the reporting time of a sales lead record and today's time is as follows:

[0167]

[0168] Among them, l is the lth clue, x 1_i Indicates the number of days between the reporting time of the i-th sales lead record and the present time;

[0169] Offset1 is the offset of the sales lead record time weight. The weight of sales lead records reported within [0, offset1] days is set to 1 and remains unchanged. The weight starts to decay after offset1 day.

[0170] scale1 is the decay rate of the sales lead record time weight, that is, the speed at which the sales lead record time weight changes;

[0171] decay1 is the sales lead record time weight that decays from 0 days to the sales lead record time weight value corresponding to scale1;

[0172] For each sales lead record, this function can be used to obtain the time weight value of the interval between the sales lead record reporting time and the present time;

[0173] Reference parameter values: offset1=14, scale1=90, decay1=0.5;

[0174] Thus,

[0175] The sales lead activity score is the sum of the scores of all sales lead records reported by the sales lead.

[0176] It should be noted that business personnel determine the parameter value as the interval between the lead record reporting time and the present time based on business rules. The weight value remains at 1 within 14 days, begins to decay after 14 days, and decays to 0.5 after 90 days. The parameters are 14, 90, and 0.5 in the formula.

[0177] The sales lead scoring design method and application suitable for user value analysis described in this embodiment, the sales lead effective follow-up score in S4)

[0178] The specific calculation process is as follows:

[0179]

[0180] Among them, i represents the i-th sales lead, j represents the j-th indicator, and biz-weigh j Indicates the business weight of the j-th indicator, data_weigh j Indicates the data weight of the j-th indicator, x ij It represents the effective follow-up score of the i-th sales lead under the j-th indicator.

[0181] The business weights of the three major indicators in the sales lead effective follow-up score are as follows:

[0182] The number of effective follow-up statistics is the sum of the number of effective follow-up times by all sales personnel since the lead was created. Although the number of effective follow-up is not the only determining factor, the lack of sufficient follow-up times often leads to the loss of sales opportunities, so it is of high importance.

[0183] The effective follow-up rate is calculated as the percentage of effective follow-up times out of all follow-up times. A high effective follow-up rate indicates that the salesperson has high execution and conversion rates, high positive customer feedback rates, and high importance.

[0184] The interval since the last effective follow-up reflects the salesperson's continued attention to and response speed to sales leads. A shorter interval indicates that the salesperson is able to follow up with customers in a timely manner and maintain communication. However, if the sales lead itself has lost its vitality or the customer has already made a purchase decision, then even a shorter interval may not change the final outcome, so its importance is lower.

[0185] The sales lead scoring design method and application for user value analysis described in this embodiment is as follows: the effective follow-up index score x of the i-th sales lead under the j-th index ij The calculation method is as follows:

[0186] In the same cycle, sales have different follow-up strategies for customers with different postgraduate entrance examination years. Therefore, when calculating the follow-up index score, the index values ​​are standardized based on the different postgraduate entrance examination years of the customers, that is,

[0187]

[0188] Among them, mean is the mean of the index values ​​grouped by postgraduate entrance examination year, and std is the standard deviation of the index values ​​distributed by postgraduate entrance examination year, so that the index values ​​of different years all satisfy the normal distribution with a mean of 0 and a standard deviation of 1.

[0189] The sales lead scoring design method and application for user value analysis described in this embodiment, wherein the three indicators of the sales lead effective follow-up score are the number of effective follow-ups, the effective follow-up rate, and the interval from the last effective follow-up.

[0190] Among them, the number of effective follow-up times and the effective follow-up rate are positive indicators, and the larger the indicator value, the higher the score; the interval since the last effective follow-up is a negative indicator, and the smaller the indicator value, the higher the score:

[0191] Based on the indicator standardization results, the sigmoid function is used to calculate the scores of each sales lead indicator. The calculation formula is as follows:

[0192] Positive indicators are:

[0193] Negative indicators are:

[0194] Among them, s ij It is the normalized result of the index value of the i-th sales lead under the j-th index.

[0195] The sales lead score design method and application for user value analysis described in this embodiment, the calculation process of the final sales lead score in S8) is as follows:

[0196]

[0197] Among them, i represents the i-th sales lead, j represents the j-th dimension, weigh1 i The weight of the ith sales lead outside the dimension is 1, that is, the weight of the interval between the creation of the sales lead and the present;

[0198] weigh2 i Indicates the weight of the i-th sales lead outside the dimension 2, that is, the weight of whether it is the customer's first sales lead, D_weigh j Indicates the weight of the j-th dimension, D_score ij represents the score of the i-th sales lead under the j-th dimension, and the weigh2 i It is 1 or 0.5. As can be seen from the formula, the out-of-dimension weight refers to the secondary weighting after the dimension score is calculated.

[0199] The weigh1 i is calculated as follows:

[0200] The formula for setting the time weight for the sales lead creation interval is as follows:

[0201]

[0202] Among them, i represents the i-th sales lead, x 2_i Indicates the number of days since the creation of the i-th sales lead.

[0203] weigh1 i The out-of-dimension weight of the i-th sales lead is 1, that is, the time weight of the sales lead creation interval;

[0204] offset2 is the offset of weight 1 outside the sales lead dimension. The weight of sales leads created within [0, offset2] days is set to 1 and remains unchanged. The weight starts to decay after offset2 days.

[0205] scale2 is the decay rate of the weight 1 outside the sales lead dimension, that is, the speed at which the weight 1 outside the sales lead dimension changes;

[0206] decay2 is the value of the weight 1 outside the sales lead dimension that decays from 0 days to the weight 1 outside the sales lead dimension corresponding to scale2;

[0207] For each sales lead, this function can be used to obtain the value of weight 1 outside the sales lead dimension;

[0208] Reference parameter values: offset2=14, scale2=180, decay2=0.5.

[0209] Example 2

[0210] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the sales lead score design method suitable for user value analysis as described above is implemented.

[0211] The sales lead score design method for user value analysis described in this embodiment is the same as that in Example 1.

[0212] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements can be made without departing from the principles of the present invention. These improvements should also be regarded as the scope of protection of the present invention.

Claims

1. A sales lead scoring design method suitable for user value analysis, characterized by: Includes the following steps: S1): Circle the sales leads that should be included in the scoring from the database. The sales leads that should be included in the scoring must meet the following requirements: the sales leads are not closed, the leads are not invalid sales leads, and the sales leads are not in the transaction pool; S2): Calculate the basic sales lead profile score based on multiple dimensions; the multiple dimensions are the user's postgraduate entrance examination year, sales stage, and contact method; S3): Calculate the sales lead activity score. Use the TF-IDF algorithm to calculate the weight of the sales lead record generation method, calculate the time interval between the sales lead record and the sales lead record reported by the sales lead, and use the Gaussian decay function to calculate the time weight. The sum of the product of the weight of the sales lead record generation method and the time weight is used as the sales lead activity score. S4): Calculate the effective follow-up score of sales leads. Select three indicators based on the sales team's effective follow-up of sales leads. Standardize the indicator values ​​according to the sales lead's application year. Use the sigmoid function to calculate the score of each indicator. Use the entropy weight method to calculate the indicator data weight. Business personnel determine the indicator business weight based on business rules. The sum of the product of the sales lead's effective follow-up indicator score, the indicator data weight, and the indicator business weight is used as the sales lead effective follow-up score. S5): Calculate the preliminary score of the sales lead. The sales personnel will determine the weight of each dimension based on the importance of each dimension and the reliability of the information source. The sum of the product of the sales lead score in each dimension and the dimension weight is used as the preliminary score of the sales lead; S6): Calculate the weight 1 outside the sales lead dimension, based on the fact that the shorter the interval between the creation time and the present, the less information the sales lead itself carries; in addition to the sales lead dimension score, calculate the interval between the creation time of the sales lead and the present, and use the Gaussian decay function to calculate the weight 1 outside the sales lead dimension. The weight value within the interval is kept at 1. The queen's weight begins to decay, After the weight decays to ; It is the offset of weight 1 outside the sales lead dimension; S7): Determine whether the sales lead is the client's first sales lead. Based on the particularity of the education industry, we introduce the idea that the higher the average order value of a sales lead, the lower the likelihood of a repeat sales lead. Calculate the weight of the sales lead dimension as 2. The weight of the first sales lead remains at 1, and the weight of the non-first sales lead is reduced to 0.

5. S8): Calculate the final sales lead score, that is, take the product of the preliminary score of the sales lead calculated in S5), the weight 1 outside the sales lead dimension in S6), and the weight 2 outside the sales lead dimension in S7) as the final sales lead score.

2. The sales lead scoring design method for user value analysis according to claim 1, characterized in that: The specific process of calculating the sales lead basic profile score in S2) based on the predicted application year, sales stage, and approachability of the selected sales leads is as follows: in, For the clues, For the Item dimension, Indicates the The weight of the item dimension, For the Under the dimension Score of sales leads; in {1, 2, 3}, respectively corresponding to the three dimensions of the customer's postgraduate entrance examination year, lead sales stage and customer contact method; the sales lead basic portrait score is composed of the three dimensions of the user's postgraduate entrance examination year, lead sales stage and customer contact method.

3. The sales lead scoring design method for user value analysis according to claim 2, characterized in that: The scores for the user's postgraduate entrance examination year, lead sales stage, and customer reachability are calculated as follows: When the dimension is the year the user took the postgraduate entrance examination, the score calculation is as follows: The user's postgraduate entrance examination year is a label for the user's postgraduate entrance examination year. Since a customer's course purchase intention is related to the purchase time and the customer's postgraduate entrance examination year, customers in different time periods and different preparation periods have different course purchase needs. Therefore, the user's postgraduate entrance examination year score is split into different months for calculation; If the sales lead does not have a postgraduate entrance examination year label, it indicates that the user has not left any intention information; if the sales lead has a postgraduate entrance examination year label that has passed, it indicates that the customer currently has no intention to purchase courses again; If a sales lead has the tag "this year's postgraduate entrance examination," the customer is more likely to purchase high-priced courses from January to June of the year of the exam. During the summer vacation from July to August, there is still a tendency to purchase courses for the second half of the exam preparation. However, from September to December, as the exam approaches, the tendency to purchase this year's postgraduate entrance examination courses decreases and tends to zero. If the sales lead has a label indicating that the postgraduate entrance examination will be held next year or later, the customer's preparation path is relatively long, and the user's willingness to purchase courses increases gradually. However, the closer the postgraduate entrance examination year is to the current year, the greater the willingness to purchase courses. Therefore, for sales leads without a postgraduate entrance examination year label or a label that has passed the postgraduate entrance examination year, the user's postgraduate entrance examination year is calculated as follows: ,in, For the clues; If a sales lead is extracted with the tag "this year's postgraduate entrance examination year", the score for that year is calculated as follows: in, For the clues, The month corresponding to the statistical date is an integer in the range [1,12]; The curve for this year's postgraduate entrance examination label is The slope of the curve, that is, the rate at which the curve decays in July and August; The curve for this year's postgraduate entrance examination label is The additional attenuation coefficient is used to accelerate the attenuation of the curve from September to December; If a sales lead is extracted with the tag "Next Year's Postgraduate Entrance Examination Year", the score for that year is calculated as follows: If a sales lead with the tag "Future Postgraduate Entrance Examination Year" is extracted and the user's postgraduate entrance examination year is later than next year, the score for the postgraduate entrance examination year is calculated as follows: The slope of the curve for next year's postgraduate entrance examination label, The slope of the curve for the future postgraduate entrance examination label later than next year, that is, the growth rate and meets , the larger and The value will make the postgraduate entrance examination score grow faster near the center point, and the smaller and The value will make the growth of the postgraduate entrance examination score more gradual; This is the center point of the curve for next year's postgraduate entrance examination label. The center point of the curve for the future postgraduate entrance examination label later than next year is the point where the score increases fastest in the postgraduate entrance examination year; When the dimension is lead sales stage, the calculation method is as follows: The lead sales stage is recorded in the CRM system, and the sales staff will mark the sales lead after following up; Sales leads can be in any of the above sales stages before winning a deal, so the sales stage score is based on the historical winning sales lead data. The proportion of sales leads in a certain sales stage to all winning sales leads is the sales stage score, that is, in, For the clues; When the dimension is customer reachability, the score is calculated as follows: The customer's contactability is recorded in the CRM system, which is composed of whether the customer has a mobile phone number or system account, whether the customer has a valid friend relationship, and whether the customer has a valid group chat relationship. If the customer has a mobile phone number or system account, sales staff can reach the customer through outbound calls, SMS, and APP private messages; If the customer has a valid friend relationship, the salesperson can communicate with the customer directly through WeChat for Business or other software; If the customer has an active group chat relationship, the salesperson can reach the customer by sending group chat messages; Customers can be reached in multiple ways at the same time. The more diverse the customer's reachable ways are, the more diverse the salesperson's reach methods are, and the higher the possibility of reaching the customer. Therefore, the reachable way score is based on historical winning sales lead data, that is, in For the clues, For the A reachable way.

4. The sales lead scoring design method for user value analysis according to claim 1, characterized in that: The specific algorithm for calculating the weight value of the sales lead record generation method based on the TF-IDF algorithm in S3) is as follows: The weight of the sales lead record generation method is calculated based on the TF-IDF algorithm. TF-IDF is TF*IDF, which is the term frequency * inverse text frequency index. In this context, the term frequency is analogous to the proportion of sales lead records corresponding to a sales lead generation method to all sales lead records for a certain winning sales lead. The inverse text frequency index calculation formula is: Where D is the total number of won leads, and d is the number of leads with a certain lead generation method. This can be compared to the ratio of all won leads to the number of leads with a certain lead generation method. To avoid this value being zero and increase discrimination, the denominator is divided by 1 and logarithmic transformation is performed. Therefore, the weight value of the sales lead record generation method = word frequency * ; The time weight calculation process of the sales lead activity score is as follows: The sales lead activity score is a comprehensive score calculated based on the sales lead records reported by the sales lead, the weight of the sales lead record generation method calculated through historical win data, and the time factor taken into account; Based on the idea that the longer the gap between a sales lead record and today's reporting time, the less important it is, time weighting can emphasize the importance of the latest sales lead record while not neglecting the contribution of sales lead records reported earlier. The time weight is based on the Gaussian decay function, and the formula for setting the time weight of the gap between the reporting time of a sales lead record and today's time is as follows: in, For the clues, Indicates the The number of days between the reporting time of the sales lead record; Set the offset of the time weight for sales leads. The weight of sales lead records reported within the day remains unchanged at 1. The queen's weight begins to decline; The decay rate of the sales lead record time weight, that is, the speed at which the sales lead record time weight changes; The weight of sales lead record time decays from 0 days to The corresponding sales lead record time weight value; For each sales lead record, this function can be used to obtain the time weight value of the interval between the sales lead record reporting time and the present time; Thus, the activity score of a single sales lead record = word frequency * ; The sales lead activity score is the sum of the scores of all sales lead records reported by the sales lead.

5. The sales lead scoring design method for user value analysis according to claim 1, characterized in that: The specific calculation process of the sales lead effective follow-up score in S4) is as follows: in, Indicates the sales leads, Indicates the Items Indicates the The business weight of each indicator, Indicates the The data weight of each indicator, Indicates the Under the indicator Sales leads effective follow-up indicator score.

6. The sales lead scoring design method for user value analysis according to claim 5, characterized in that: The said Under the indicator Sales leads effective follow-up indicators The calculation method is as follows: within the same period, sales have different follow-up strategies for customers with different postgraduate entrance examination years. Therefore, when calculating the follow-up index score, the index values ​​are standardized based on the different postgraduate entrance examination years of the customers, that is, in, For the Under the indicator Standardized results of the effective follow-up indicator values ​​of sales leads; is the mean of the index values ​​grouped by postgraduate entrance examination year, is the standard deviation of the distribution index values ​​of the postgraduate entrance examination years, so that the index values ​​of different years all satisfy the normal distribution with a mean of 0 and a standard deviation of 1.

7. The sales lead scoring design method for user value analysis according to claim 5, characterized in that: The three major indicators of the sales lead effective follow-up score are the number of effective follow-up times, the effective follow-up rate and the interval from the last effective follow-up. Among them, the number of effective follow-up times and the effective follow-up rate are positive indicators, and the larger the indicator value, the higher the score should be; The interval from the last effective follow-up is a negative indicator. The smaller the indicator value, the higher the score should be: Based on the indicator standardization results, the sigmoid function is used to calculate the scores of each sales lead indicator. The calculation formula is as follows: Positive indicators are: Negative indicators are: in, For the Under the indicator The standardized results of the effective follow-up indicator values ​​of sales leads.

8. The sales lead scoring design method for user value analysis according to claim 1, characterized in that: The calculation process of the final sales lead score in S8) is as follows: in, Indicates the sales leads, Indicates the Item dimension, Indicates the The weight outside the sales lead dimension is 1, that is, the weight of the interval since the sales lead was created; Indicates the The weight outside the sales lead dimension is 2, that is, the weight of whether it is the customer's first sales lead. Indicates the The weight of the item dimension, Indicates the Under the dimension Score of sales leads; described is calculated as follows: The formula for setting the time weight for the sales lead creation interval is as follows: in, Indicates the sales leads, Indicates the The number of days between the creation time of sales leads and now, Indicates the The weight outside the sales lead dimension is 1, that is, the weight of the time interval between the creation of the sales lead and the present; For the offset of weight 1 outside the sales lead dimension, set The weight of sales leads created within the first day remains unchanged at 1, and the weight starts to decrease after the first day. is the decay rate of the weight 1 outside the sales lead dimension, that is, the speed at which the weight 1 outside the sales lead dimension changes; The weight of the sales lead dimension decays from 0 days to The corresponding value of weight 1 outside the sales lead dimension; For each sales lead, this function can be used to obtain the value of weight 1 outside the sales lead dimension.

9. The sales lead scoring design method for user value analysis according to claim 8, characterized in that: described is 1 or 0.

5.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the sales lead score design method suitable for user value analysis according to any one of claims 1 to 9 are implemented.

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