Model: arad1367/technographics-marketing-matryoshka
Author: arad1367
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:396, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, en, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:BAAI/bge-base-en-v1.5, base_model:finetune:BAAI/bge-base-en-v1.5, license:apache-2.0, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: BAAI/bge-base-en-v1.5
Card metadata: base_model: BAAI/bge-base-en-v1.5
language: en
library_name: sentence-transformers
license: apache-2.0
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:396, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: How can technographics contribute to predicting consumer behavior in digital marketing?
sentences: Data analysis is essential in predicting consumer behavior in digital marketing. Analysis of data related to consumer behavior, preferences and needs can reveal patterns and trends that can be used to forecast future behavior and refine marketing strategies., Technographics enables businesses to understand the technological habits and preferences of their customers. By analyzing this data, companies can predict how these users are likely to interact with their digital products or services, and tailor their marketing strategies accordingly., The key components include data collection (gathering data from various sources), data analysis (using algorithms and models to analyze data), and predictive modelling (predicting future customer behavior based on analyzed data)., source_sentence: How is technographic data collected for understanding cross-channel behavior?
sentences: Technographic data for understanding cross-channel behavior is collected through various data analytics tools that track the customer's interactions across different digital channels. These tools can monitor website usage, mobile app activity, social media engagements, and email click-through rates., Adobe's "Real-Time Marketing Insights" is a notable case. They utilized technographic data to identify their customer's most-used digital tools, leading to significant enhancements in their personalized marketing strategies and an increase in customer engagement., Technology stack analysis helps identify the tools and platforms a company uses for digital marketing. It enables marketers to understand the infrastructure that supports their strategies and spot opportunities for innovation or consolidation., source_sentence: How does consumer behavior pattern analysis contribute to the effectiveness of digital marketing campaigns?
sentences: Machine learning can identify patterns and trends in technographic data that may not be obvious to humans. It can also predict future behavior based on these patterns, allowing businesses to anticipate consumer needs and adjust their strategies accordingly., Consumer behavior pattern analysis provides insights into how, when, and why consumers interact with digital marketing content. These insights can be used to refine campaign strategies, enhance personalization, and ultimately improve conversion rates and customer loyalty., Predictive analytics can forecast what content will resonate best with certain audience segments based on past engagement. It can guide topics, formats, and delivery channels, enabling marketers to create content that is more likely to attract and engage their target audience., source_sentence: What are technographics in the context of digital marketing?
sentences: Technographics is a market research analysis method that investigates the technology-related behaviors and preferences of consumers. This includes their usage, adoption and purchase of technology, which is crucial in forming a comprehensive understanding of your target audience's digital landscape., Technographics data can be collected through surveys, social media mining, or purchased from data providers. The data is analyzed using statistical techniques or machine learning algorithms to identify patterns and insights related to consumer behavior., Technographics can help businesses understand what platforms or technologies their competitors' customers use, providing insights into competitor tech strengths and weaknesses. This can guide businesses in differentiating their offers and positioning themselves more effectively in the market., source_sentence: How important is it to update technographic data frequently?
sentences: By analyzing a competitor's technology stack, marketers can gain insights into their strategies, tools, and platforms. This knowledge can help them identify gaps in their own stack, adopt superior technologies, or find ways to differentiate their approach., It is crucial. Technology trends and usage patterns evolve quickly. Keeping your technographic data up-to-date ensures that your marketing strategies remain relevant and effective., Technographics is a research methodology that provides data about consumers based on their technology use, preferences and behavior. This method helps businesses understand which technologies their audience is using, and how they use them, thereby, informing the development of more effective and personalized marketing strategies.
model-index: name: Technographics Marketing Matryoshka
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 768
type: dim_768
metrics: type: cosine_accuracy@1
value: 0.37373737373737376
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.5050505050505051
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5757575757575758
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.7575757575757576
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.37373737373737376
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.16835016835016833
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.11515151515151514
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.07575757575757575
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.37373737373737376
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.5050505050505051
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5757575757575758
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.7575757575757576
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.5323267552745661
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.46469456469456494
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.47723382714423296
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 512
type: dim_512
metrics: type: cosine_accuracy@1
value: 0.37373737373737376
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.5151515151515151
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5757575757575758
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.7272727272727273
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.37373737373737376
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.17171717171717168
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.11515151515151514
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.0727272727272727
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.37373737373737376
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.5151515151515151
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5757575757575758
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.7272727272727273
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.5279877868900206
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.46727994227994235
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.4818097786730832
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 256
type: dim_256
metrics: type: cosine_accuracy@1
value: 0.35353535353535354
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.48484848484848486
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5858585858585859
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.7070707070707071
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.35353535353535354
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.16161616161616163
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.11717171717171715
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.07070707070707069
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.35353535353535354
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.48484848484848486
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5858585858585859
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.7070707070707071
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.5102400942328595
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.44968734968734975
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.4654526924283992
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 128
type: dim_128
metrics: type: cosine_accuracy@1
value: 0.37373737373737376
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.47474747474747475
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5757575757575758
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.6868686868686869
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.37373737373737376
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.15824915824915825
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.11515151515151512
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.06868686868686867
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.37373737373737376
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.47474747474747475
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5757575757575758
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.6868686868686869
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.5096813265364254
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.45540724707391383
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.4713790516617994
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 64
type: dim_64
metrics: type: cosine_accuracy@1
value: 0.3434343434343434
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.48484848484848486
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5353535353535354
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.6868686868686869
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.3434343434343434
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.16161616161616163
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.10707070707070705
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.06868686868686867
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.3434343434343434
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.48484848484848486
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5353535353535354
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.6868686868686869
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.4979120019313254
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.44037197370530706
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.4556726424225123
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 88
Created: 2025-02-07 15:29:13