{"id":4284,"date":"2014-10-10T13:38:36","date_gmt":"2014-10-10T20:38:36","guid":{"rendered":"https:\/\/www.kochava.com\/?p=359"},"modified":"2022-08-18T15:19:31","modified_gmt":"2022-08-18T22:19:31","slug":"kochava-brings-data-science-mobile-attribution-analytics","status":"publish","type":"post","link":"https:\/\/www.kochava.com\/ko\/blog\/kochava-brings-data-science-mobile-attribution-analytics\/","title":{"rendered":"Kochava Brings Data Science to Mobile Attribution Analytics"},"content":{"rendered":"[vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_spacing=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text]The Kochava Data Science team provides supercharged insight into users\u2019 behavior, habits, and traffic patterns. By mining vast amounts of data and using predictive analysis techniques the Data Science team can identify actionable insights into your business. Kochava Data Science powers Fraud Detection<\/a>, Television Lift Attribution<\/a>, and our game-changing Kochava Optimization Beacon<\/a>.\r\n\r\nThe Kochava Data Science team was featured on VentureBeat, when it was first announced in October, 2014. \u00a0Read the article below, or check it out on Venturebeat here.<\/a>\r\n\r\nKochava<\/a>, a mobile analytics and attribution outfit based in Idaho, hired a three-member data scientist team in a bid to help mobile advertisers avoid retargeting fraud and make better sense of the vast amounts of data needed to drive successful mobile ad campaigns. \u201cThat\u2019s the idea behind it. This whole concept of investing in a data science team that\u00a0is putting together analyses on top of our data, on behalf of our customers, I think will further move the ball with regard to the maturity and expectation in mobile media buying,\u201d Kochava chief executive Charles Manning told VentureBeat today. Mobile analytics technology provides brands and marketers with detailed intelligence on consumers\u2019 buying habits and where best to focus and deploy campaigns. Analytics enables clients like Coca-Cola or\u00a0Tesla, for example, to help measure, via downloads and clicks, whether the campaign in question is a success, or failure.\r\n\r\nAnalysts who study the burgeoning mobile ad space, currently dominated by Google and Facebook in terms of revenue, see a potential trend with Kochava\u2019s decision to build a data science team internally instead of outsourcing requests. \u201cAbsolutely. I see this as a leading edge of a potential new trend,\u201d Andrew Frank, an analyst at Gartner,<\/a>\u00a0told VentureBeat. \u201cIn terms of measuring metrics, how do you analyze the data for mobile? It\u2019s still very fragmented,\u201d Frank added. The mobile ecosystem has exploded in the last year alone. Many analytics firms currently rely on their own measurement technology to help clients decide where to buy inventory to drive downloads, for example. So, while not totally new, hiring data scientists to help sift information points may become commonplace as analytic companies vie amongst themselves for a piece of a sector that will crest $35 billion by year\u2019s end, according to\u00a0eMarketer.<\/a> Peter Hamilton, chief executive of successful analytics stalwart\u00a0Tune<\/a>, said the skillsets of data scientists complement the space. \u201cThere are two types of data scientists in organizations like ours. One type is\u00a0scaling out the way we actually record and measure all the data coming into our servers on behalf of our customers,\u201d he said. \u201cAnd the other side are more like BI\u00a0[business intelligence]\u00a0scientists who are looking for trends and insights in the data,\u201d Hamilton added. \u201cThey might be only analyzing the business itself and how\u00a0customers\u00a0interact<\/span><\/a>\u00a0with it, or they may be providing insights or reporting for clients to better understand their own data.\u201d For Manning, pulling the trigger on in-house data-scientist hires makes sense in both the short and long term. \u201cThe value, succinctly, is looking at trend information in aggregate, and understanding things like fraud, retargeting, and cross-app churn,\u201d he said. \u201cIn other words, how are users being acquired\u00a0in one app, or potentially moving from one app to the other, within a customer\u2019s portfolio?\u201d Manning said. \u201cGetting that data, and understanding it, is pretty meaningful,\u201d he said.[\/vc_column_text][\/vc_column][\/vc_row]","protected":false},"excerpt":{"rendered":"

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