# SBL Success Stories Published campaign case studies from https://sbl.so/case-study/ This feed contains only published, password-free stories. Results are campaign-specific claims from the linked original articles, not guaranteed outcomes. Drafts and proposed stories are excluded. ## 58 contacts replied from 126 reached: LinkedIn networking for machine learning careers URL: https://sbl.so/case-study/linkedin-linkedin-networking-for-machine-learning-careers-cs024/ Industry: Recruitment & Careers Channel: LinkedIn An anonymized LinkedIn campaign offering Applied machine-learning candidacy and career conversations to Hiring professionals in machine learning. Replies are measured within 14 days of the first qualifying outreach. Reported results: - 58 Contacts who replied (14 days) - 126 Contacts reached - 46.0% 14-day reply rate Adapt this approach: 1. Choose a specific audience: Hiring professionals in machine learning. 2. Make one offer easy to understand: Applied machine-learning candidacy and career conversations. 3. Ask permission to share a short overview, and let the reply determine the next step. Reading the numbers: Replies are any recorded replies, not verified positive interest. Connection acceptances, meetings, sales and revenue are not established for this campaign. Counts use unique contact records in the defined 14-day cohort. Figures are reported in the published story, not independently re-audited here. Original case study: 58 of 126 contacts replied within 14 days — a 46.0% reply rate in this campaign’s measured outreach cohort. This case examines the audience, offer and recorded follow-up activity behind that result. The business context The campaign was configured around Applied machine-learning candidacy and career conversations. Its intended audience was Hiring professionals in machine learning. The useful message-design angle is Use hiring criteria and career questions as the opening to an opportunity discussion. A reader considering a similar campaign can use this context to judge whether the offer and audience fit their own business; the reply count alone does not establish that fit. What the campaign record shows The measured first outreach ran from 4 April 2026 to 20 April 2026. The source snapshot was taken on 15 October 2026, with a complete 14-day observation window for every included contact. 126 contact records were reached through qualifying delivered or read outreach. 58 contacts replied within 14 days, equivalent to 46.0% of the measured cohort. The cohort contained 421 outbound messages and 176 incoming messages inside those windows. Message totals include multiple messages per contact. How the conversation unfolded Among the 58 responding contacts, 24 replied within 24 hours and 57 within seven days. The median time to a first reply was about 24.3 hours, measured only among contacts who replied. 98 contacts received more than one outbound message before their first reply or the end of their 14-day window. This describes the observed follow-up coverage; it does not prove that follow-ups caused the replies. Keep follow-up coverage active across the observation window; the reply record was not confined to the first day. What to do in a similar campaign Choose a specific audience: Hiring professionals in machine learning. Make one offer easy to understand: Applied machine-learning candidacy and career conversations. Ask permission to share a short overview, and let the reply determine the next step. Track the stages separately: a reply, a positive response, a booked conversation and a completed sale answer different questions. Keep the campaign objective explicit before deciding whether a reply is a useful business outcome. A first message you can adapt An adaptable example based on the campaign’s offer; this is not a verbatim customer message. Hi [First name], thought this might be relevant to your work: Applied machine-learning candidacy and career conversations. Would a short overview be useful?Replace the greeting and add only context you can verify. If the offer needs explanation, keep that explanation relevant to the recipient’s work rather than adding a long list of features. A follow-up with a fresh reason to reply Hi [First name], one detail that may help: [a concrete example, useful resource or answer to a likely question]. Happy to share more if this is relevant.This follow-up is a recommended adaptation, not a recovered message from the original campaign. About these numbers This is an observational case study from SBL’s LinkedIn snapshot, not a controlled test or a client interview. Contact counts are unique lead records within this campaign. Sender-contact pairs with earlier inbound messages or exposure to multiple recorded campaigns are excluded; contacts with multiple eligible sender records are deduplicated. Only delivered/read, nonempty outbound messages and recorded incoming text are counted. Replies are any recorded replies, not verified positive interest. Connection acceptances, meetings, sales and revenue are not established for this campaign. Counts use unique contact records in the defined 14-day cohort. Later activity and contacts outside this cohort are not included. Names, account identifiers and private conversation text are omitted. ---