{"id":1163,"date":"2018-01-10T07:00:24","date_gmt":"2018-01-10T13:00:24","guid":{"rendered":"https:\/\/digitalstriked.wpengine.com\/?p=1163"},"modified":"2020-04-03T14:29:02","modified_gmt":"2020-04-03T19:29:02","slug":"problem-solving-case-study","status":"publish","type":"post","link":"https:\/\/www.digitalstrike.com\/problem-solving-case-study\/","title":{"rendered":"How We Approach Problem Solving [Case Study]"},"content":{"rendered":"<p>Client A is a nationwide telecommunications company.<\/p>\n<p>They provide TV, internet, and phone services to customers across the country, primarily in Arizona, Arkansas, California, Louisiana, Missouri, North Carolina, Oklahoma, Texas, and West Virginia.<\/p>\n<p>In 2015, we started our engagement with Client A as a partner of another agency and have provided search engine optimization services since then.<\/p>\n<p>We were tasked with building domain authority and increasing traffic to the website, but in the process of doing this, we also became a digital marketing partner for the client by providing outside insights into their overall digital marketing efforts, including paid search campaigns.<\/p>\n<p>The following is a case study in how a little investigative work into the data of a paid search campaign can uncover some very helpful findings to get things turned back around.<\/p>\n<h2>The Problem<\/h2>\n<p>Every month, we report against the Return on Ad Spend for organic search traffic, branded paid search traffic, unbranded paid search traffic, and any additional paid channels that were being run as part of an experiment for new opportunities.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"size-full wp-image-1176\" src=\"http:\/\/www.digitalstrike.com\/wp-content\/uploads\/2018\/01\/Suddenlink-Case-Study-Findings.jpg\" alt=\"Return on Ad Spend Findings\" width=\"1600\" height=\"450\" srcset=\"https:\/\/www.digitalstrike.com\/wp-content\/uploads\/2018\/01\/Suddenlink-Case-Study-Findings.jpg 1600w, https:\/\/www.digitalstrike.com\/wp-content\/uploads\/2018\/01\/Suddenlink-Case-Study-Findings-300x84.jpg 300w, https:\/\/www.digitalstrike.com\/wp-content\/uploads\/2018\/01\/Suddenlink-Case-Study-Findings-1024x288.jpg 1024w, https:\/\/www.digitalstrike.com\/wp-content\/uploads\/2018\/01\/Suddenlink-Case-Study-Findings-768x216.jpg 768w, https:\/\/www.digitalstrike.com\/wp-content\/uploads\/2018\/01\/Suddenlink-Case-Study-Findings-1536x432.jpg 1536w, https:\/\/www.digitalstrike.com\/wp-content\/uploads\/2018\/01\/Suddenlink-Case-Study-Findings-846x238.jpg 846w\" sizes=\"(max-width: 1600px) 100vw, 1600px\" \/><\/p>\n<h3>Return on Ad Spend Findings<\/h3>\n<p>Within our reporting on Return on Ad Spend, we were able to establish trends in Client A\u2019s paid search performance and inconsistencies in the alignment between the SEO and PPC agencies.<\/p>\n<p><strong>Client A had changed paid search agencies in May and there were major increases in ad spend that lead to a negative ROI. <\/strong><\/p>\n<h2>Our Investigation Process<\/h2>\n<p>Client A was already aware of a few issues regarding new geographic targeting implemented by the agency currently managing the Google AdWords campaigns, but through our investigation we were able to establish additional insights into exact causes that contributed to this negative return.<\/p>\n<p>To note a caveat, we weren\u2019t actually allowed to go into the Google AdWords account directly. As a result, all data that we were able to adequately evaluate came directly from Client A&#8217;s Google Analytics account.<\/p>\n<p>From this data, we found the following:<\/p>\n<ul class=\"custom-arrow\">\n<li>~17% year-over-year (YoY) increase in paid search visits to Client A&#8217;s BuyFlow Storefront (a visitor&#8217;s initial step to completing a sale)<\/li>\n<li>This ~17% increase, however, was offset by a ~11% decrease in paid search sessions to the second step of the process<\/li>\n<li>Alarmingly, there was ~105% increase in paid search visits to their \u201cNot Serviceable\u201d page &#8212; a clear indication of the aforementioned geographical targeting issue<\/li>\n<\/ul>\n<p><strong>Ultimately, what was happening was, paid search visitors were starting the conversion process but then leaving the site when they found that they didn\u2019t live in one of Client A\u2019s service areas. <\/strong><\/p>\n<h3>State Targeting<\/h3>\n<p>Looking deeper into this ~105% YoY increase, we found that the most significant increases occurred in Texas (118%), California (331%), and Missouri (192%).<\/p>\n<p>Compared to overall traffic, <strong>there was an increase in NoService sessions in Texas of 102%, California of 188%, and Missouri of 180%.<\/strong><\/p>\n<p>While these three states are areas in which Client A provides telecommunication services, its paid search campaigns in Google AdWords were also targeting areas in which they did not provide services.<\/p>\n<h3>City Targeting<\/h3>\n<p>The three cities that produced the highest increase of &#8220;NotServiceable&#8221; sessions were Dallas (80%), Houston (135%), San Antonio (198%), and Los Angeles (302%).<\/p>\n<p>This data allowed us the opportunity to report on what we were seeing beyond the state-level and offer up suggestions on where we could clean up specific cities\/zip codes within Client A&#8217;s service areas.<\/p>\n<p>Cleaning up this data at both the state and city levels could help improve the quality of site traffic from paid search, increase Quality Scores, and thus provide an opportunity at improved conversion rates and enhanced lead generation efforts.<\/p>\n<h3>Keyword Targeting<\/h3>\n<p>Finally, we took a look at the PPC campaigns&#8217; keywords, specifically trying to identify any discrepancies that may have existed YoY to contribute to the decrease in Return on Ad Spend.<\/p>\n<p>Upon starting our keyword investigation, we noticed there were some terms added to the campaigns, and set to a broad match modified match type, that were noticeably absent from the Google Analytics data from the previous year.<\/p>\n<p>An example of one of these keywords is the term &#8220;Internet.&#8221;<\/p>\n<p>After this keyword was added to the PPC campaigns using Google AdWords&#8217; more restrictive match types &#8212; exact and phrase, in addition to broad match modified &#8212; it generated an overall spend of $122,467 and a cost-per-click of $6.47 &#8212; both of which were higher than when the less-restrictive broad match was being used exclusively ($11, 988 in ad spend; avg. CPC of $1.54).<\/p>\n<p>However, once the more restrictive match types were applied, the number of overall conversions (275) and Return on Ad Spend ($23,325) both increased significantly compared to that generated by the broad match &#8212; 149 and $11,988, respectively.<\/p>\n<p>Additionally, we found that some ad spend was being wasted on keywords that suggested a need for completely unrelated services, such as the term &#8220;uHaul.&#8221; While these irrelevant terms didn\u2019t generate a ton of waste &#8212; roughly ~$8,000 and a return of 10 confirmations or $850 ($85 value for confirmations) &#8212; this evidence suggested there was definitely an opportunity to pause some keywords to funnel more spend towards more successful keywords.<\/p>\n<p>Overall, there was a significant increase in average CPCs due to a change in match types from broad &#8212; which typically produces cheaper clicks but at the risk of less control over who sees the paid ads. The additions of the broad match modified, phrase, and exact match types drove the average CPCs up by 46 cents on branded terms and by $6.15 on unbranded terms.<\/p>\n<h2>Our Solution<\/h2>\n<p>Too many cities and states were being served ads that could not provide cable and Internet services at those locations.<\/p>\n<p>We could tell this was happening based on the increase in Not Serviceable page sessions. <strong>We recommended a closer look into the targeting by state and city.<\/strong><\/p>\n<p>When looking at the limited keyword data, <strong>we recommended they turn on a few of the campaigns that were being managed by the previous agency. <\/strong><\/p>\n<p>The goal behind this recommendation was to not only turn back on the top performing past campaigns but hopefully stabilize the loss in Return on Ad Spend and decreas the number of Not Serviceable page sessions.<\/p>\n<p>&nbsp;<\/p>\n<p><!--\n\n\n\n\n<p class=\"lead\" style=\"text-align: left;\"><span style=\"font-size: 16pt;\"><em><strong>Can't quite put your finger on what's happened to your previously successful paid search campaign? <\/strong><\/em><\/span><\/p>\n\n\n\n\n<p class=\"lead\" style=\"text-align: left;\"><span style=\"font-size: 16pt;\"><em><strong>This client couldn't either and tasked us with trying to help them solve where things are going wrong. <\/strong><\/em><\/span><\/p>\n\n\n\n\n<p><strong>Here's how we approached solving the problem.\n\n\n<\/strong><\/p>\n\n\n\n\n<h2><strong>THE CLIENT<\/strong><\/h2>\n\n\n\n\n<hr \/>\n\n\n\n\n<p>Client A is a nationwide telecommunications company.<\/p>\n\n\n\n\n<p>They provide TV, internet, and phone services to customers primarily in\u00a0Arizona, Arkansas, California, Louisiana, Missouri, North Carolina, Oklahoma, Texas, and West Virginia.<\/p>\n\n\n\n\n<p>In 2015, we started our engagement with Client A as a partner of another agency and have provided search engine optimization services since then.<\/p>\n\n\n\n\n<p>We were tasked with building authority and increasing traffic to the website, but in this process we also became a digital marketing partner by providing outside insights into their overall digital marketing, including paid search.<\/p>\n\n\n\n\n<h2>\u00a0<\/h2>\n\n\n\n\n<h2 style=\"text-align: left;\"><strong>THE PROBLEM<\/strong><\/h2>\n\n\n\n\n<hr \/>\n\n\n\n\n<p>Every month we report against the Return on Ad Spend for organic search traffic, branded paid search traffic, unbranded paid search traffic, and any additional paid channels that were being run as part of an experiment for new opportunities.<\/p>\n\n\n<span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\"><\/span>\n\n\n<p>\nIt was in our reporting on Return on Ad Spend, we were able to establish trends in Client A's paid search performance and inconsistencies in the alignment between the SEO and PPC agencies.<\/p>\n\n\n\n\n<p><strong>Client A had changed paid search agencies in May and there were major increases in spending with a negative return on ad spend.<\/strong><\/p>\n\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n\n<h2 style=\"text-align: left;\"><strong>OUR INVESTIGATION PROCESS<\/strong><\/h2>\n\n\n\n\n<hr \/>\n\n\n\n\n<p>Client A already knew a few problems regarding their new geographic targeting, but through our investigation we were able to establish additional insights into causes that contributed to this negative return.<\/p>\n\n\n\n\n<p>To caveat this a bit, we weren't actually allowed to go into the Adwords account directly and we were only able to pull adequate information from Google Analytics.<\/p>\n\n\n\n\n<p>We found that from paid search year over year (YoY), there was an increase of ~17% to the BuyFlow Storefront (there initial step to completing a sale), but this increase in the traffic came with an ~11% decrease in sessions to the second step of the process. The alarming part was the increase in visits to their \"Not Serviceable\" page by ~105% which is a clear indication of the geographic targeting issue that was established in the beginning.\u00a0<\/p>\n\n\n\n\n<p><strong>This meant that people were starting the conversion process but then found that they didn't live in Client A's service areas.\u00a0<\/strong><\/p>\n\n\n\n\n<h3>\u00a0<\/h3>\n\n\n\n\n<h3>State Targeting<\/h3>\n\n\n\n\n<hr \/>\n\n\n\n\n<p>Looking deeper into the ~105% YoY increase to the \"Not Serviceable\" page, the most significant increases by regions (state) were Texas (118%), California (331%), and Missouri (192%).<\/p>\n\n\n\n\n<p>Compared to overall traffic, <strong>there was an increase in NoService sessions in Texas of 102%, California of 188%, and Missouri of 180%.<\/strong><\/p>\n\n\n\n\n<p>While those are states in which Client A provides telecommunication services, they were targeting areas where they didn't provide any services.\u00a0<\/p>\n\n\n\n\n<h3>\u00a0<\/h3>\n\n\n\n\n<h3>City Targeting<\/h3>\n\n\n\n\n<hr \/>\n\n\n\n\n<p>Dallas (80%), Houston (135%), San Antonio (198%), and Los Angeles (302%) had the most \"Not Serviceable\" sessions.<\/p>\n\n\n\n\n<p>So this allowed us to report on not just the state level declines but also where there were opportunities to clean up specific cities\/zip codes where they can provide services.<\/p>\n\n\n\n\n<h3>\u00a0<\/h3>\n\n\n\n\n<h3>Keyword Targeting<\/h3>\n\n\n\n\n<hr \/>\n\n\n\n\n<p>Finally, we took a look at the keywords and any discrepancies that exist year over year that are contributing to this decrease in Return on Ad Spend.<\/p>\n\n\n\n\n<p>It looked like there were some additional broad match modified keywords added that weren't in Google Analytics the previous year.<\/p>\n\n\n\n\n<p>Take,\u00a0for instance,\u00a0\"internet,\" where there was a spend of $122,467.15 and an average cost per click (CPC) of $6.47 that had a return of $23.325 if we used $85 per completion. We're not completely sure of the lifetime value (<span style=\"color: #000000;\">LTV)<\/span> of a confirmation, but there were 275 confirmations from the addition of this keyword match type.<\/p>\n\n\n\n\n<p>If we compare that to last year, when internet was just using the broad match type, there was a spend of $11,988.11 at an average CPC of $1.54 that saw a return of $12,665 or 149 confirmations.\u00a0<\/p>\n\n\n\n\n<p>We also found some spend on keywords like \"uhaul\" and moving in general. While they didn't spend a lot, ~$8,000 and see a return of 10 confirmations or $850 ($85 value for confirmations), there was definitely opportunity to pause some wasteful keywords.\u00a0<\/p>\n\n\n\n\n<p>Finally, there was a significant increase in average CPC's due to a change in match types from Broad which are typically cheaper but provide less control. The additions of Broad Match Modified, Phrase, and Exact drove the average CPC's up by $0.46 CPC on branded terms and up to $6.15 CPC on unbranded terms.<\/p>\n\n\n\n\n<h2>\u00a0<\/h2>\n\n\n\n\n<h2 style=\"text-align: left;\"><strong>OUR SOLUTION<\/strong><\/h2>\n\n\n\n\n<hr \/>\n\n\n\n\n<p>There were too many cities and states that were being served ads even though they couldn't provide cable and internet services at those locations.<\/p>\n\n\n\n\n<p>We could tell this was happening based on the increase in Not Serviceable page sessions. <strong>We recommended a closer look into the targeting by state and city.<\/strong><\/p>\n\n\n\n\n<p>When looking at the limited keyword data, <strong>we recommended they turn on a few of the campaigns that were running by the previous agency<\/strong>. This was in an effort to only turn back on the top performing past campaigns and hopefully stabilize the loss in Retrunon Ad Spend while decreasing the number of Not Serviceable page sessions.<\/p>\n\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n\n<p class=\"lead\" style=\"text-align: center;\"><strong>To learn more about our PPC services, <a href=\"http:\/\/www.digitalstrike.com\/contact\/\" target=\"_blank\" rel=\"noopener noreferrer\">contact us for a free consultation<\/a>.<\/strong><\/p>\n\n\n\n\n--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Client A is a nationwide telecommunications company. They provide TV, internet, and phone services to customers across the country, primarily in Arizona, Arkansas, California, Louisiana, Missouri, North Carolina, Oklahoma, Texas, and West Virginia. In 2015, we started our engagement with Client A as a partner of another agency and have provided search engine optimization services [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":2148,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[48,11,13],"tags":[44,22,34,49,40],"class_list":["post-1163","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-case-studies","category-ppc","category-solution-partner","tag-case-study","tag-pay-per-click","tag-ppc","tag-problem-solving","tag-solutions-partner"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.digitalstrike.com\/wp-json\/wp\/v2\/posts\/1163","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.digitalstrike.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.digitalstrike.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.digitalstrike.com\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/www.digitalstrike.com\/wp-json\/wp\/v2\/comments?post=1163"}],"version-history":[{"count":0,"href":"https:\/\/www.digitalstrike.com\/wp-json\/wp\/v2\/posts\/1163\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.digitalstrike.com\/wp-json\/wp\/v2\/media\/2148"}],"wp:attachment":[{"href":"https:\/\/www.digitalstrike.com\/wp-json\/wp\/v2\/media?parent=1163"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.digitalstrike.com\/wp-json\/wp\/v2\/categories?post=1163"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.digitalstrike.com\/wp-json\/wp\/v2\/tags?post=1163"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}