Data-driven sales optimization with regression and chaotic pattern search
Sandhya Rani Gaddam, Sarada Jayan, Pentakota Ravi, Bilal Alatas
TL;DR
This paper presents a data-driven approach to optimize sales by predicting the best time to convert leads into customers using a novel algorithm.
Contribution
The novel Hybrid Chaotic Pattern Search Algorithm (HCPSA) is introduced to determine optimal conversion timelines for sales leads.
Findings
Patterns in lead data were used to predict the next activity for qualified leads.
HCPSA identified the optimal number of days required to convert leads into customers.
The approach helps prioritize leads and improve sales conversion efficiency.
Abstract
Lead generation is the process of gaining potential customers’ interest to increase future sales, and it is an essential part of many businesses’ (amusement parks, theme parks, clubs, etc.) sales processes as their membership is more expensive. The main objective of these businesses is to increase the count of customers. By generating sales leads, a club/park can find leads who have already expressed interest in its products and services and access their audience potential, allowing them to focus on future marketing and sales efforts on those leads that are more likely to convert. The current work focuses on how to convert a lead to a customer in optimum number of days. We collect two kinds of data: customer data and lead generation data. The customer data consists of all the leads who have taken the membership, and the lead generation data consists of all current leads. The details of…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42
Figure 43
Figure 44
Figure 45
Figure 46
Figure 47
Figure 48
Figure 49
Figure 50Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDutch Social and Cultural Studies · Educational Assessment and Improvement · Mathematics Education and Teaching Techniques