DigitalMR

DigitalMR is a British analytics company that does business as listening247 and provides social listening, sentiment analysis and related marketing tools^[1][2]. The company analyzes online conversations to identify topics, intent and sentiment, and connects those outputs with content-generation and social-media workflow products^[1][5].

DigitalMR announced the listening247 rebrand in 2024; an externally distributed report of the announcement describes listening247, DataVinci, engaging247 and communities247 as the company's product family^[3]. The legal name DigitalMR Ltd remains in the website copyright and terms material^[1].

What It Does

The listening247 platform collects and analyzes unstructured conversations from digital channels and converts them into measures of subjects, intentions and sentiment^[1][2]. The company presents this process as social intelligence for understanding brand perception, customer concerns and changes in online discussion^[1].

The service is not limited to monitoring. DataVinci uses analytical output to generate proposed advertising copy, social posts and visual briefs, while social-media management functions support approval and publishing^[1][3]. These generated materials are separate from the observed conversation data and require editorial and brand review.

Data and Methodology

The company says its engine ingests real-time social conversations and applies artificial-intelligence models to annotate topics, intent and sentiment^[1][2]. A published customer testimonial describes a project in which approximately 30,000 Spanish-language posts from three Latin American countries were labeled for beer-brand sentiment and an independent party appointed by the customer measured 87 percent precision^[1]. This is evidence from one disclosed project, not a universal accuracy rate.

An index developed with Glion Institute of Higher Education illustrates another methodology: it used English-language posts and metadata from Instagram, TikTok, X, news, forums, blogs and YouTube, together with Google search trends, to assess luxury brands over a specified period^[2]. Source mix, language, spam filtering, entity resolution and model training can materially affect any social-listening result.

Products

The portfolio includes Social Listening and Analytics, DataVinci for advertising automation and targeting, influencer-marketing tools, communities247 for private stakeholder communities, and social-media post creation and publishing^[1][2]. The company also offers dashboards and case-specific analysis for brand-health tracking^[1].

Private communities collect solicited feedback from invited participants, whereas social listening analyzes unsolicited material available through covered sources^[2][3]. The distinction affects consent, representativeness and interpretation: neither source should automatically be treated as a population survey.

Buyers and Use Cases

The official site organizes offerings for creators, small and medium-sized businesses and large enterprises^[1]. Uses include brand monitoring, campaign evaluation, identifying common customer objections, influencer analysis, market research, content planning and maintaining research communities^[1][2].

Independent publication Research World featured listening247's chief executive in a discussion of remote research and the changing relationship between social intelligence and direct questioning^[4]. For alternative-data users, online sentiment and discussion volume may complement surveys or financial data, but platform demographics and changes in source access can create breaks in a time series.

Delivery and Pricing

The website offers demonstrations and describes dashboards, analytics services and integrated workflows rather than publishing a standard price list^[1][2]. Prospective customers must obtain current terms for the required sources, languages, query volume, historical period and service level.

The company reports offices or contact locations in London and Austin^[1][2]. No independently verified revenue, funding or customer-count figure is established by the sources cited here, so such totals are omitted.

Data Considerations

Social-listening datasets can contain personal data, deleted posts, automated accounts and copyrighted material. Buyers should document source permissions, retention rules, geographic restrictions, model training data and procedures for responding to deletion or access requests.

Sentiment is context-dependent and can be affected by sarcasm, dialect, topic and the target entity. Project-level validation against human annotation, such as the disclosed beer example, is more informative than assuming that one accuracy figure transfers across brands, languages or channels^[1].