{"id":442412,"date":"2021-06-22T05:31:46","date_gmt":"2021-06-22T12:31:46","guid":{"rendered":"https:\/\/www.esri.com\/about\/newsroom\/?post_type=wherenext&p=442412"},"modified":"2021-11-19T14:28:20","modified_gmt":"2021-11-19T22:28:20","slug":"insurance-and-location-intelligence","status":"publish","type":"wherenext","link":"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/insurance-and-location-intelligence","title":{"rendered":"A Growing Company Enlists the Four A\u2019s of Location Intelligence"},"author":501,"featured_media":0,"parent":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"sync_status":"","episode_type":"","audio_file":"","podmotor_file_id":"","podmotor_episode_id":"","castos_file_data":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":"","_links_to":"","_links_to_target":""},"categories":[369852,1001,369792],"tags":[571,303812,441081,1641,324332],"department":[476822],"wherenext-category":[],"industry":[],"class_list":["post-442412","wherenext","type-wherenext","status-publish","format-standard","hentry","category-artificial-intelligence--ai","category-commercial","category-location-intelligence","tag-analytics","tag-business-planning","tag-geoai","tag-insurance","tag-market-intelligence","department-data-and-ai"],"acf":{"short_description":"With artificial intelligence, automation, cloud-based architecture, and analytics, an insurance company executes an ambitious growth plan.","pdf":{"host_remotely":false,"file":"","file_url":""},"flexible_content":[{"acf_fc_layout":"content","content":"Sometimes it takes an audacious, even intimidating goal to inspire a company to find new levels of innovation."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"Article snapshot:<\/strong> Facing an ambitious revenue target, an innovative company employed new digital techniques, including the four A\u2019s of location intelligence, to expand its business and stay ahead of the competition.","snippet":""},{"acf_fc_layout":"content","content":"That was the thinking behind the ambitious growth strategy launched in 2016 by an insurance firm based in the Midwest. With agile startups beginning to crowd the insurance market, company executives sought to reach more commercial and personal clients who could benefit from the firm\u2019s high-touch, consultative approach. The senior leadership team set a goal big enough to catalyze new approaches to reach that customer base: grow its annual premium by hundreds of millions of dollars\u2014responsibly and effectively for customers.\r\n\r\nThe growth plan was two-pronged: find new clients in existing markets throughout the company\u2019s multistate territory, while also expanding into new geographies. Since the company sells policies through a network of thousands of independent agencies, executives needed to understand which offices and geographies showed the greatest potential for growth\u2014and they had to do so using data to supplement their instincts. To expand at the scale and pace of the strategic plan, company leaders knew they needed data technology that could pinpoint and predict high-performing opportunities. They didn\u2019t realize that would lead them to the four best practices of location intelligence.<\/a>\r\n

Mining Data for Competitive Advantage<\/strong><\/h3>\r\nOne of the first steps C-suite executives took was to hire a data and analytics officer from a financial institution\u2014an industry where the ability to mine information for competitive advantage was already a main differentiator of success. He then recruited a team of data scientists and analysts with experience in big data applications like machine learning algorithms."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"Applying the Four A\u2019s of Planning to Real Time Decisions<\/strong>\r\n\r\nThe four A\u2019s of location intelligence give business leaders strategic insight to plan long-term investments. It also equips front-line workers with real-time information to make more immediate decisions\u2014for example, relocating workers away from a storm or dispatching a driver to deliver mobile food orders to the most efficient sequence of addresses. For more on these \u201cbusiness decisions at the edge,\u201d read this WhereNext<\/em> article<\/a>.","snippet":""},{"acf_fc_layout":"content","content":"The company\u2019s aggressive growth plans were not without complications. Insurance providers must expand carefully\u2014by appropriately pricing the risk they take on and monitoring the amount of risk undertaken in any one market.\r\n\r\nThis required a granular understanding of the benefits and hazards of territories where the company planned to expand. By employing a geographic information system (GIS<\/a>), the data science team could use location intelligence, or location-based business insights, to uncover promising new business avenues while protecting the firm from excessive risk.\r\n\r\n\u201cWe believe it\u2019s a competitive advantage,\u201d says the company\u2019s chief strategy officer. \u201cThe increase in the amount of data available, the variables available, the computing power available, the tools available\u2014it\u2019s going to separate winners and losers over time, and we intend to be a winner.\u201d\r\n

The Four A\u2019s of Location Intelligence<\/strong><\/h3>\r\nThe data science team maximized the impact of location-based insights by employing complementary technologies and executing on the four A\u2019s of location intelligence<\/a>: analytics, cloud-based architecture, artificial intelligence (AI)<\/a>, and automation.\r\n\r\nAnalytics lay at the heart of the work done by the team, as they used GIS technology to segment the entire United States into millions of grid cells complete with over 1,000 variables, including demographic, geographic, and firmographic data. This data-rich grid acted as a basemap for growth, delivering geospatial context on which regions were fertile for business opportunity, while spotlighting risks like coastal flooding or earthquake activity that might weigh against issuing certain policies."},{"acf_fc_layout":"form","form_type":"aside","form_position":"Right","form_title":"THE ESRI BRIEF","form_desc":"A biweekly email connecting senior executives and business leaders with thought-provoking articles on location intelligence and critical technology trends.","form_button_label":"Sign up now","form_content":"https:\/\/go.pardot.com\/l\/82202\/2017-10-12\/jw1bmb","form_tag":"low-commitment-form\/sign-up-form"},{"acf_fc_layout":"content","content":"AI, in the form of machine learning, paired with GIS (a combination known as GeoAI<\/a>) enabled the data science team to create algorithms that predicted the revenue potential of regions and ranked the performance potential of agencies the company might partner with. Automation sped up processes and made analysis more efficient, saving costs and streamlining labor. A cloud-based architecture supported the massive computational effort required to crunch millions of data points and support machine learning engines.\r\n\r\nWith those four pillars of location intelligence in place, employees from sales managers up to the CFO could make decisions knowing they had access to the best information and location-based data. \u201cWe\u2019re getting new insights about what\u2019s working and what isn\u2019t in our business, and then deriving actions as to what we should do about it,\u201d the analytics officer explains. \u201cThen we can predict and scenario test what\u2019s likely to happen. That hedges our investment in a really positive way.\u201d\r\n\r\nA few years into the expansion effort, company leaders can already point to success, enabled in part by the data-powered efforts. The firm has grown its commercial lines premium by almost a third since 2016. Over the same period, the average premium per agency has increased by 115 percent."},{"acf_fc_layout":"quote","image":442392,"text":"Powered by data savvy, an innovative corporate culture, and location intelligence, the company is on its way to attaining its strategic goals and outperforming the industry on growth and profitability.","author_name":"","author_profession_organization":""},{"acf_fc_layout":"content","content":"

Finding New Leads with Machine Learning<\/strong><\/h3>\r\nThe machine learning<\/a> algorithms core to the copmpany\u2019s data-powered growth helped accelerate and improve market analysis. The analytics team employed AI to comb through enormous datasets and produce conclusions and predictions on a scale the human mind couldn\u2019t match. Combined with geographic context from location intelligence, machine learning algorithms gave executives insight on where growth looked most promising.\r\n\r\nBy feeding the algorithms huge amounts of company and third-party data, the analytics team could teach models to recognize the variables that signal growth potential. Based on analysis of historical patterns, the algorithms can recognize that an agency located in a town with a certain set of demographic and geographic attributes should generate a certain level of business.\r\n\r\nThe resultant GeoAI-powered map helped automate processes, saving the sales team hours, if not days, by singling out the best leads for new distribution points in areas where the firm had no presence.\r\n\r\nInstead of guesswork, decision-makers draw conclusions based on facts and patterns surfaced by AI.\r\n

A Map Key to Communication and Risk Assessment <\/strong><\/h3>\r\nThe segmented smart map made it easier to share information related to the growth plan across departments. Instead of searching through files or charts, colleagues consulted a GIS-powered dashboard showing the location of partner agencies, competitive agencies, and possible areas of expansion.\r\n\r\nThe analysis was further refined to reflect different lines of business, which depended on different data inputs and were projected to grow at varying rates. Using GeoAI-based analytics, the team segmented those variables and delivered useful smart maps to executives responsible for each line."},{"acf_fc_layout":"quote","image":442442,"text":"When we talk about GIS . . . it can apply to all aspects of our business, whether that's identifying where customers are and what their needs are, or distribution. ","author_name":"Chief strategy officer","author_profession_organization":""},{"acf_fc_layout":"content","content":"

Risk Assessment in the Age of Climate Change<\/strong><\/h3>\r\nExecutives knew that growth at any price wasn\u2019t smart growth, so they focused on sustainable ways to expand. Location intelligence enabled them to move into new territories at a fast clip while sidestepping rising threats from climate-related volatility and natural disasters. Company underwriters could look at current policies and literally see on a map where they stood in relation to existing or potential hazards.\r\n\r\nFor example, the data analytics team helped build a tool that allowed underwriters to plug any address into a search bar. An automated geocoding service calculates how far that point is from the closest shoreline. This capability gives underwriters a better sense of threats to a property or business from events like hurricanes or other severe weather<\/a>, resulting in an analytics-backed assessment of whether to assume the risk in the first place."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"A Faster Route to Processing Claims<\/strong>\r\n\r\nIn addition to boosting the company-wide growth plan, the data science team helped streamline daily operations at the insurance firm. Smart maps allowed claims managers to speed up what was once a multistep process of organizing claims and manually plotting them with pen and paper. With the help of GIS, managers can now document all the day\u2019s claims on a dashboard, and dispatch adjusters based on their suitability to handle the claim as well as their geographic proximity. The technology also helped adjusters optimize routes so they weren\u2019t doubling back or wasting miles on the road<\/a>\u2014saving time for themselves and their clients.","snippet":""},{"acf_fc_layout":"content","content":"Underwriters used the same tool to analyze existing policies and identify misclassified risks. Some policies were actually closer to the coastline than had initially been recorded, while others were farther away. In both cases, accurate geospatial insights helped the company fulfill the core business function of appropriately taking risk.\r\n\r\nAutomation aided the process by adding new or updated data to the national grid. \u201cThat\u2019s a big time savings just from a model maintenance standpoint,\u201d the analytics director says. \u201cIt frees up our data scientists to do more value-add work rather than updating and changing data.\u201d\r\n\r\nThe four A\u2019s of location intelligence also make it possible to contextualize information that might otherwise be treated as disconnected data points, discarded, or shrugged at in other firms. Instead, analysts were able to discover patterns that make the company more competitive.\r\n\r\nFor instance, a hot spot analysis of claims around one Ohio city revealed areas where customers were reporting damages at higher levels than seen in the past. By overlaying data like population growth over the geographic clusters where claim activity increased, managers could determine whether the uptick was simply due to a larger volume of residents. That insight could trigger the company to allocate more claims adjusters to the area.\r\n\r\nAlternately, if those claims happened to coincide with clusters of hail storms or other climate-related trends, the information might lead executives to slow the pace of policy writing there, or change the kind of products available."},{"acf_fc_layout":"quote","image":442432,"text":"The future is: how do we leverage . . . additional data variables, how do we become more predictive, and how do we manage the risk more effectively?","author_name":"Chief strategy officer ","author_profession_organization":""},{"acf_fc_layout":"content","content":"

A Suite of Data Technologies <\/strong><\/h3>\r\nCompany leaders began with an ambitious goal, then put the right talent in place. The data science team translated the strategic ambition into algorithms and smart maps that could point the company toward growth, agency by agency, juxtaposed against potential risk. That GIS-powered location intelligence was made possible by a suite of technologies working in tandem: the insights of analytics, the predictive modeling of artificial intelligence, the efficiency of automation, and the processing power of the cloud.\r\n\r\n\u201cWhether it\u2019s our heat maps or our competitor footprint on there, everyone gets it,\u201d the analytics director says. \u201cIt\u2019s immediately obvious what you\u2019re trying to do. You want quick understanding, you want recall, and hopefully it drives quick action. 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