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Lawyer States How Omotosho Was Unprofessional, Biased In Nnamdi Kanu’s Case January 29, 2026 10:52amEmail: newsroom@osazuwaakonedo.news
Lawyer States How Omotosho Was Unprofessional, Biased In Nnamdi Kanu’s Case January 29, 2026 10:52am
24-Year-Old Thai Citizen Arrested With Cannabis In Lagos
https://osazuwaakonedo.news/24-year-old-thai-citizen-arrested-with-cannabis-in-lagos/23/02/2025/
#NDLEA #Thailand #Anambra #Keffi #Lagos #Nasarawa #Onitsha ©February 23rd, 2025 ®February 23, 2025 2:12 pm Men of the Nigeria anti illicit drugs police, National Drug Law Enforcement Agency, NDLEA have arrested a female citizen of Thailand with quantities of cannabis in Lagos State, the former capital city of Nigeria; just as NDLEA intelligent operatives fortnight ago bursted transborder drug dealers in Keffi area of Nasarawa state and Onitsha area of Anambra state with the recovering of multi-million-naira opioids from Sienna bus bumper and meth in auto spare part. #OsazuwaAkonedo
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Law Enforcement Agency NDLA have arrested a female citizen of
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Thailand with quantities of cannabis in Lego State, the former
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capital city of Nigeria, just as nd L EA intelligence
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operatives fortnight Agobaster transmordered drug dealers in Kfe area of
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Nasarawa State and Uniture area of unambrustates with the recovery
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of multimillion Nira opioids from Selenamu’s Bompa and met in
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Auto Spare part the Thai lani was arrested at Legos
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Airport with boxes of illicit drug consignments, according to news
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release meant available by the nd L spokesperson Feri Barba
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Fermi on Sunday, NDLA, in the news release also intercepted
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UK bound drug shipments concealed in worlds of created cartoons.
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Details of the news releases rates in cold stores are
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tailed by a twenty four year old Thai lady part
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of a womenard to smaller forty three parceles of Canadian
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loud A synthetics trend of cannabis wearhing forty six point
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six zero kilograms into Nigeria through the Mottalia Mohanand International
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Airport Mmia Ekja Legos has been fatned by operatives of
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the National Drug Law Enforcement Agency NDLA who arrested her
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after discovering the illicit consignment in her boxes. The suspect,
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who confessed to be a higher drug trafficker, was arrested
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on Thursday, the twenty eeth of February twenty twenty five
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during the inmant clearance of passengers on Kata always flight
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from Talan via Doha Kata at the arrival Hall Teminal
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two of the Legos Airport. She sent the drug Katail,
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which recruited her promised to pay her three thousand dollars
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upon successful delivery of the illicit drug consignment in Nigeria.
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Another maide by a drug trafficking syndicate to shift sixty
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eight parzles of Ghanian loud with a total weight of
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forty two point two kilograms concealed in walls of created
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cartoons to London, UK through the export shard of the
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Legos Airport, was also frustrated by NDLEA officers. On Friday,
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the twenty first of February, three suspects a fret agent
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and two dispatch riders were initially arrested in connection with
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the seashore before the mastermind of the shipment, Samuel Bitrice,
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was swiftly traced to his Exodos estate, ajar Legos home,
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where he was arrested at the Port Harcourt Port Complex,
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Arnie River State and LA officers on Thursday, the twentieth
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of February intercepted forty nine cartoons containing forty nine thousand
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pills of Tamil, a brand of Drama Don two hundred
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and twenty five milligrams in a forty fifth container during
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a joint examination of the shipment with men of Customs
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Service and other security agencies in Nasarawa State and LA
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operatives on Saturday, the twenty second of February arrested two
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suspects mellow Adamu forty and piles at Zuka forty two
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at cocoona Kfe with five hundred and seventeen kilograms of skunk,
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while two other suspects usman Rowa forty three and Unisaharula
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forty five, when aped in the Toyota Corolla car, conveyed
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sixty two point seven kilograms of skunk along sample Assibti Road, Contagura,
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Nijester on Thursday, the twentieth of February, with the Seal
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Vigo commands and formations of the agency across the country
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continued their war against drug abuse wider sensitizational activities to schools,
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worship centers, workplaces and communities among others. In the past week.
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These include Wilder sensitization lecture to students and staff of
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Marco Grammar School, Tapa Oil State, Comprehensive Secondary School or
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Rugway in both states, to Islamia Secondary School Ampa, Kogi
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State and wary About Primary School, Ugu, Naba Emo State,
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among others. While commanding the officers and men of m
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m I A, PHPC, Nijer and Nasalla commands of the
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agency for the arrests and c shows, Chairman Chief Executive
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Officer of nd L e A Brigadia, General Mohammed Bubamawa
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Retired stated that their operational successes and those of their
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compatriots across the country, especially their balanced approach to drug
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supply reduction and drug demand reduction efforts, are well appreciated.
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Nd LA must trans bordered drug KATIL arrests four members
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in Honicha Kfee operatives of the National Drug Law Enforcement
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Agency n d l EA have mustered a trans bordered
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drug trafficking syndicate, arresting four members in intelligence led operations
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during which multimillion naira wars of dramadol pills concealed in
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the bomber and false bottom of Senna bosses held them
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to border towns were recovered. Following weeks of intelligence and
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souverillance operations by operatives of the Directorate of Intelligence in NDLA,
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a Senna Boss marked a BJ four five two HG
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was intercepted at Nasarawatutu Road KFI, Nasarawa State on Tuesday
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the twenty seventh of January twenty twenty five at six
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a m. Two suspects, Zardin Adamun twenty seven and Abubaka
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00:05:31,879 –> 00:05:36,759
Usman forty four were arrested in the vehicle coming from Nicha,
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Anambra State on rout Yola, Adamawa State. A second Sienna
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Boss driven by Aba Usman forty eight was also intercepted.
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During a search of the two vehicles, specially constructed still
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compartments were discovered after the removal of the back bomba
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where a total of one hundred and ninety nine hundred
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and sixty pills of tramadol were consumed and in the
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space designed to house the spur tire of the vernicools.
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Investigations revealed that an Hoornitcher, an Ambra State best Della
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Kingsley Uvari, was the supplier of the seas tramadol consignment.
87
00:06:16,160 –> 00:06:18,959
A sweet follow up operation led to the arrest of
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Mubari at his Ugar Street Onnitcha home on the twenty
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ninth of January. Two vernicouls, a Toyota Corolla car mart
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FGG nine hundred and forty eight megafarats and the Toyota
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00:06:31,399 –> 00:06:35,240
Sena Boss marked gw A twenty three hh were recovered
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from his house. In another interdiction, operatives of the Intelligence
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Department intercepted a commercial was coming from Hoornitcha, an Amber State,
94
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at Abadjiche Point FCT, Abuja on the fourth of February.
95
00:06:50,160 –> 00:06:53,519
A passenger in the Boss chairman Sir Henry O Jinuas
96
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thirty two, carrying motor spe parts in the black bag,
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00:06:57,759 –> 00:07:02,519
was arrested when his was searched. Four hundred and four
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00:07:02,600 –> 00:07:06,519
point four seven grounds of metam fetermine five hundred and
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00:07:06,680 –> 00:07:10,199
six point four nine grounds of laud, a synthetic strand
100
00:07:10,240 –> 00:07:12,959
of canomies, and two hundred and sixty two point three
101
00:07:13,079 –> 00:07:17,120
two grounds of dimetl sulfone, a precosso substance for mixing myth,
102
00:07:17,519 –> 00:07:20,160
all of which were concealed in the motor paths were
103
00:07:20,199 –> 00:07:26,319
recovered in cross river state. NYLA officers on patrol along
104
00:07:26,439 –> 00:07:31,000
Ogoja Abakaliki Road yeah on Thursday, the thirteenth of February
105
00:07:31,079 –> 00:07:34,720
intercepted one hundred and seventy thousand pills of tramadol in
106
00:07:34,759 –> 00:07:38,959
a truck driven by Paul Chukudi thirty one, while operatives
107
00:07:39,040 –> 00:07:41,920
at the Apapa Syport in Legos on Tuesday, the eleventh
108
00:07:42,000 –> 00:07:45,519
of February recovered eighty five thousand, four hundred bottoms of
109
00:07:45,639 –> 00:07:50,680
codein based syrup in a container imported from India. Two suspects,
110
00:07:51,040 –> 00:07:54,399
harleno Isa and gambo Omaru, were not at Bama Road.
111
00:07:54,879 –> 00:07:58,920
My Dewbury bernostaed on Friday the fourteenth of February with
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00:07:59,079 –> 00:08:03,519
sixty compressed blocks of skunk, a strand of kanabis weighing
113
00:08:03,639 –> 00:08:08,920
seventy two kilograms, while ADLA operatives in Ekiti States on Tuesday,
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00:08:08,959 –> 00:08:13,199
the eleventh of February reded a notorious drug joined at
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00:08:13,279 –> 00:08:18,160
Ilodas Street, Ekola, Ekiti, where they arrested three suspects Bala
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00:08:18,240 –> 00:08:25,079
Mohammed fifty seven, Yahya Adamu fifty five and Abdulahi Hehu thirty.
117
00:08:25,959 –> 00:08:29,399
A total of eighty three kilograms of skunk were recovered
118
00:08:29,439 –> 00:08:33,600
from them. In Legos, twenty eight year old sayad O
119
00:08:33,679 –> 00:08:37,480
Labisi Yusuf was on Saturday, the fifteenth of February arrested
120
00:08:37,519 –> 00:08:40,480
by operatives during a raid of Harleki home where one
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00:08:40,600 –> 00:08:44,000
hundred and sixty nine point five meters of nitrous oxide
122
00:08:44,039 –> 00:08:47,320
popularly known as laughing glass and one hundred and eleven
123
00:08:47,480 –> 00:08:51,440
grounds of love were recovered. Not less than one hundred
124
00:08:51,559 –> 00:08:54,639
and thirty two point eight four kilograms of skunk were
125
00:08:54,720 –> 00:09:00,440
recovered from two suspects, Ibrahim Busman forty and Solomon Gms
126
00:09:00,759 –> 00:09:05,279
forty in Taraba, while Osman was marked with eighty eight
127
00:09:05,399 –> 00:09:09,279
kilograms of arizona and gunner. Lud at Wukari Gems was
128
00:09:09,399 –> 00:09:12,440
arrested with forty four points at four kilograms of same
129
00:09:12,519 –> 00:09:18,600
substance at Takanaba Sabulgari in Jalingo Local Government area. Meanwhile,
130
00:09:19,039 –> 00:09:24,720
across the country. NDLA commands continued their war against drug abuse,
131
00:09:25,240 –> 00:09:32,320
Wada sensitization lectures and advocacy visits to warship centers, schools, workplaces,
132
00:09:32,759 –> 00:09:37,000
placies of traditional rulers and communities all through the past week.
133
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Instances include Wada Sensitization lecture to students and staff of
134
00:09:42,840 –> 00:09:49,240
Government Technical College, Oku Aqua Ibom Future Leader’s Academy, Kishi,
135
00:09:49,720 –> 00:09:57,519
Ohio State Government Secondary School, Bashir Sokoto Er Comprehensive School,
136
00:09:57,879 –> 00:10:03,879
Obar Hill, Abia Government Day Secondary School, Torah, Jegawa State
137
00:10:04,399 –> 00:10:10,000
and Jesuit College Armado Akit, Akit State, while Kano State
138
00:10:10,159 –> 00:10:14,240
Commander of ld L e A conducted Wader Enlightenment Electure
139
00:10:14,360 –> 00:10:17,720
for married women in PRADA at marcas Uma Katoff Center,
140
00:10:18,200 –> 00:10:22,919
Kanu among others. While commanding the officers and men of
141
00:10:23,080 –> 00:10:30,000
Directorate of Intelligence are Papa Cross River Bernu, Akit, Taraba
142
00:10:30,519 –> 00:10:33,639
and Legos commands of the agency for the arrests and
143
00:10:33,799 –> 00:10:38,240
secures of the past week. Chairman Chief Executive Officer of
144
00:10:38,519 –> 00:10:43,399
ld L e A, Regainia, General Mohammed Bubamawa Retired, also
145
00:10:43,519 –> 00:10:46,960
applauded their compatriots in all the commands across the country
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for intensifying the word sensitization, lectures and advocacy messages to
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every part of the areas of responsibility. Ferly Baba Family Director,
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Media and Advocacy nd l EA started on Sunday, the
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sixteenth of February twenty twenty five posaslre On Neddy, bringin You,
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00:11:09,799 –> 00:11:11,799
bring me in a mortgagital way,

Osazuwa Akonedo is a Nigerian media professional, author, and consultant. His work spans various sectors of communication and literature:
Osazuwa Akonedo is a Nigerian media creator, journalist, and entrepreneur who engages audiences via news reporting, social media content, music, and a mobile application.
Osazuwa Akonedo is associated with OSAZUWAAKONEDO LIMITED, a company registered in Nigeria that operates in media consultancy and news services
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Pinterest is a visual discovery platform for sharing and exploring creative ideas through images and links.
Pinterest Tag is a web analytics service that tracks and reports website traffic.
Service URL: policy.pinterest.com (opens in a new window)
A social media platform for discussion, sharing content, and community engagement across various topics and interests.
Smartadserver is a comprehensive ad management platform for publishers and advertisers, optimizing digital advertising campaigns.
A multimedia messaging app for sharing photos, videos, and stories that disappear after viewing.
Taboola is a content discovery platform that recommends articles and videos to engage users across the web.
A social media platform for creating, sharing, and discovering short videos, often featuring music and creative effects.
TikTok Pixel is a tracking tool that measures user interactions and optimizes ad campaigns on the TikTok platform.
Service URL: ads.tiktok.com (opens in a new window)
X, formerly known as Twitter, is a social networking and microblogging platform.
Service URL: x.com (opens in a new window)
X Pixel enables businesses to track user interactions and optimize ad performance on the X platform effectively.
Service URL: x.com (opens in a new window)
Yahoo offers a web portal, search engine, and email services, providing news, finance, and entertainment content.
A video-sharing platform for users to upload, view, and share videos across various genres and topics.
Service URL: www.youtube.com (opens in a new window)
These purposes are part of the IAB Transparency & Consent Framework.
Cookies, device or similar online identifiers (e.g. login-based identifiers, randomly assigned identifiers, network based identifiers) together with other information (e.g. browser type and information, language, screen size, supported technologies etc.) can be stored or read on your device to recognise it each time it connects to an app or to a website, for one or several of the purposes presented here.
Examples:
Most purposes explained in this notice rely on the storage or accessing of information from your device when you use an app or visit a website. For example, a vendor or publisher might need to store a cookie on your device during your first visit on a website, to be able to recognise your device during your next visits (by accessing this cookie each time).
Advertising presented to you on this service can be based on limited data, such as the website or app you are using, your non-precise location, your device type or which content you are (or have been) interacting with (for example, to limit the number of times an ad is presented to you).
Examples:
A car manufacturer wants to promote its electric vehicles to environmentally conscious users living in the city after office hours. The advertising is presented on a page with related content (such as an article on climate change actions) after 6:30 p.m. to users whose non-precise location suggests that they are in an urban zone.
A large producer of watercolour paints wants to carry out an online advertising campaign for its latest watercolour range, diversifying its audience to reach as many amateur and professional artists as possible and avoiding showing the ad next to mismatched content (for instance, articles about how to paint your house). The number of times that the ad has been presented to you is detected and limited, to avoid presenting it too often.
Information about your activity on this service (such as forms you submit, content you look at) can be stored and combined with other information about you (for example, information from your previous activity on this service and other websites or apps) or similar users. This is then used to build or improve a profile about you (that might include possible interests and personal aspects). Your profile can be used (also later) to present advertising that appears more relevant based on your possible interests by this and other entities.
Examples:
If you read several articles about the best bike accessories to buy, this information could be used to create a profile about your interest in bike accessories. Such a profile may be used or improved later on, on the same or a different website or app to present you with advertising for a particular bike accessory brand. If you also look at a configurator for a vehicle on a luxury car manufacturer website, this information could be combined with your interest in bikes to refine your profile and make an assumption that you are interested in luxury cycling gear.
An apparel company wishes to promote its new line of high-end baby clothes. It gets in touch with an agency that has a network of clients with high income customers (such as high-end supermarkets) and asks the agency to create profiles of young parents or couples who can be assumed to be wealthy and to have a new child, so that these can later be used to present advertising within partner apps based on those profiles.
Advertising presented to you on this service can be based on your advertising profiles, which can reflect your activity on this service or other websites or apps (like the forms you submit, content you look at), possible interests and personal aspects.
Examples:
An online retailer wants to advertise a limited sale on running shoes. It wants to target advertising to users who previously looked at running shoes on its mobile app. Tracking technologies might be used to recognise that you have previously used the mobile app to consult running shoes, in order to present you with the corresponding advertisement on the app.
A profile created for personalised advertising in relation to a person having searched for bike accessories on a website can be used to present the relevant advertisement for bike accessories on a mobile app of another organisation.
Information about your activity on this service (for instance, forms you submit, non-advertising content you look at) can be stored and combined with other information about you (such as your previous activity on this service or other websites or apps) or similar users. This is then used to build or improve a profile about you (which might for example include possible interests and personal aspects). Your profile can be used (also later) to present content that appears more relevant based on your possible interests, such as by adapting the order in which content is shown to you, so that it is even easier for you to find content that matches your interests.
Examples:
You read several articles on how to build a treehouse on a social media platform. This information might be added to a profile to mark your interest in content related to outdoors as well as do-it-yourself guides (with the objective of allowing the personalisation of content, so that for example you are presented with more blog posts and articles on treehouses and wood cabins in the future).
You have viewed three videos on space exploration across different TV apps. An unrelated news platform with which you have had no contact builds a profile based on that viewing behaviour, marking space exploration as a topic of possible interest for other videos.
Content presented to you on this service can be based on your content personalisation profiles, which can reflect your activity on this or other services (for instance, the forms you submit, content you look at), possible interests and personal aspects. This can for example be used to adapt the order in which content is shown to you, so that it is even easier for you to find (non-advertising) content that matches your interests.
Examples:
You read articles on vegetarian food on a social media platform and then use the cooking app of an unrelated company. The profile built about you on the social media platform will be used to present you vegetarian recipes on the welcome screen of the cooking app.
You have viewed three videos about rowing across different websites. An unrelated video sharing platform will recommend five other videos on rowing that may be of interest to you when you use your TV app, based on a profile built about you when you visited those different websites to watch online videos.
Information regarding which advertising is presented to you and how you interact with it can be used to determine how well an advert has worked for you or other users and whether the goals of the advertising were reached. For instance, whether you saw an ad, whether you clicked on it, whether it led you to buy a product or visit a website, etc. This is very helpful to understand the relevance of advertising campaigns.
Examples:
You have clicked on an advertisement about a “black Friday” discount by an online shop on the website of a publisher and purchased a product. Your click will be linked to this purchase. Your interaction and that of other users will be measured to know how many clicks on the ad led to a purchase.
You are one of very few to have clicked on an advertisement about an “international appreciation day” discount by an online gift shop within the app of a publisher. The publisher wants to have reports to understand how often a specific ad placement within the app, and notably the “international appreciation day” ad, has been viewed or clicked by you and other users, in order to help the publisher and its partners (such as agencies) optimise ad placements.
Information regarding which content is presented to you and how you interact with it can be used to determine whether the (non-advertising) content e.g. reached its intended audience and matched your interests. For instance, whether you read an article, watch a video, listen to a podcast or look at a product description, how long you spent on this service and the web pages you visit etc. This is very helpful to understand the relevance of (non-advertising) content that is shown to you.
Examples:
You have read a blog post about hiking on a mobile app of a publisher and followed a link to a recommended and related post. Your interactions will be recorded as showing that the initial hiking post was useful to you and that it was successful in interesting you in the related post. This will be measured to know whether to produce more posts on hiking in the future and where to place them on the home screen of the mobile app.
You were presented a video on fashion trends, but you and several other users stopped watching after 30 seconds. This information is then used to evaluate the right length of future videos on fashion trends.
Reports can be generated based on the combination of data sets (like user profiles, statistics, market research, analytics data) regarding your interactions and those of other users with advertising or (non-advertising) content to identify common characteristics (for instance, to determine which target audiences are more receptive to an ad campaign or to certain contents).
Examples:
The owner of an online bookstore wants commercial reporting showing the proportion of visitors who consulted and left its site without buying, or consulted and bought the last celebrity autobiography of the month, as well as the average age and the male/female distribution of each category. Data relating to your navigation on its site and to your personal characteristics is then used and combined with other such data to produce these statistics.
An advertiser wants to better understand the type of audience interacting with its adverts. It calls upon a research institute to compare the characteristics of users who interacted with the ad with typical attributes of users of similar platforms, across different devices. This comparison reveals to the advertiser that its ad audience is mainly accessing the adverts through mobile devices and is likely in the 45-60 age range.
Information about your activity on this service, such as your interaction with ads or content, can be very helpful to improve products and services and to build new products and services based on user interactions, the type of audience, etc. This specific purpose does not include the development or improvement of user profiles and identifiers.
Examples:
A technology platform working with a social media provider notices a growth in mobile app users, and sees based on their profiles that many of them are connecting through mobile connections. It uses a new technology to deliver ads that are formatted for mobile devices and that are low-bandwidth, to improve their performance.
An advertiser is looking for a way to display ads on a new type of consumer device. It collects information regarding the way users interact with this new kind of device to determine whether it can build a new mechanism for displaying advertising on this type of device.
Content presented to you on this service can be based on limited data, such as the website or app you are using, your non-precise location, your device type, or which content you are (or have been) interacting with (for example, to limit the number of times a video or an article is presented to you).
Examples:
A travel magazine has published an article on its website about the new online courses proposed by a language school, to improve travelling experiences abroad. The school’s blog posts are inserted directly at the bottom of the page, and selected on the basis of your non-precise location (for instance, blog posts explaining the course curriculum for different languages than the language of the country you are situated in).
A sports news mobile app has started a new section of articles covering the most recent football games. Each article includes videos hosted by a separate streaming platform showcasing the highlights of each match. If you fast-forward a video, this information may be used to select a shorter video to play next.
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